MicLocation.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NLMJLQA0pcUU"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "#from pandas import read_csv\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import optimizers\n",
        "from keras.layers import Dropout\n",
        "# from tensorflow.python.keras.layers import Dense\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "# from sklearn.datasets import make_regression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "# from sklearn.metrics import cross_entropy\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "oAu3DewkphrJ",
        "outputId": "fcaa94cb-a4bd-4da2-cf96-8b11a971dc76"
      },
      "outputs": [
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              "        0    1    2         3         4         5         6         7  \\\n",
              "0     7.5  4.6  3.1  2.212121  2.054545  1.663636  2.260586  2.160668   \n",
              "1     7.5  4.6  3.1  2.212121  2.054545  1.663636  2.471220  2.177877   \n",
              "2     7.5  4.6  3.1  2.212121  2.054545  1.663636  2.148735  2.048493   \n",
              "3     7.5  4.6  3.1  2.212121  2.054545  1.663636  2.417868  2.227188   \n",
              "4     7.5  4.6  3.1  2.212121  2.054545  1.663636  2.246036  2.231220   \n",
              "...   ...  ...  ...       ...       ...       ...       ...       ...   \n",
              "4995  8.0  6.0  3.0  2.525253  3.737374  1.989899  2.130309  4.090720   \n",
              "4996  8.0  6.0  3.0  2.525253  3.737374  1.989899  2.571669  4.048508   \n",
              "4997  8.0  6.0  3.0  2.525253  3.737374  1.989899  2.553296  3.361803   \n",
              "4998  8.0  6.0  3.0  2.525253  3.737374  1.989899  2.850498  3.864035   \n",
              "4999  8.0  6.0  3.0  2.525253  3.737374  1.989899  2.477794  3.412987   \n",
              "\n",
              "             8  \n",
              "0     1.639225  \n",
              "1     1.543440  \n",
              "2     1.909046  \n",
              "3     1.710995  \n",
              "4     1.516712  \n",
              "...        ...  \n",
              "4995  2.100784  \n",
              "4996  1.797962  \n",
              "4997  1.885446  \n",
              "4998  2.080539  \n",
              "4999  1.866763  \n",
              "\n",
              "[5000 rows x 9 columns]"
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              "      <td>2.525253</td>\n",
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              "      <td>4.048508</td>\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "# Load and Preprocess Data\n",
        "data = pd.read_csv('/content/Updated_List.csv', header=None)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1D6a2e8PpxR6"
      },
      "outputs": [],
      "source": [
        "# Normalize the whole dataset\n",
        "# Separate room dimensions from speaker and mic coordinates\n",
        "room_dimensions = data.iloc[:, :3]\n",
        "speaker_coordinates = data.iloc[:, 3:6]\n",
        "mic_coordinates = data.iloc[:, 6:]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_max = room_dimensions.iloc[:, 0].max()\n",
        "X_max"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "csN4d6TU3qqf",
        "outputId": "ea9734b8-87c9-4608-d7cf-eefe93d87bbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12.4"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X_min = room_dimensions.iloc[:, 0].min()\n",
        "# X_min"
      ],
      "metadata": {
        "id": "glK_gMMG5CWS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_max = room_dimensions.iloc[:, 1].max()\n",
        "Y_max"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LCY-nI6Q5zqe",
        "outputId": "f2507f02-691b-4d2d-d07f-7375f7d9fb1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.6"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Y_min = room_dimensions.iloc[:, 1].min()\n",
        "# Y_min"
      ],
      "metadata": {
        "id": "Mh2BHJGb6MQ5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Z_max = room_dimensions.iloc[:, 2].max()\n",
        "Z_max"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gg6K67NS6TY-",
        "outputId": "9cda4000-e504-4063-ea55-d08c96d34405"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.4"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Z_min = room_dimensions.iloc[:, 2].min()\n",
        "# Z_min"
      ],
      "metadata": {
        "id": "jaZfpQXe6jMq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5J5g8MIZFFyt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vG5k-Jt7qDM9"
      },
      "outputs": [],
      "source": [
        "# Normalizing the room dimensions X-coordinates\n",
        "max=1\n",
        "min=0\n",
        "room_X = room_dimensions.iloc[:, 0]/ X_max\n",
        "room_scaled_X = room_X * (max - min) + min"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing the room dimensions Y-coordinates\n",
        "max=1\n",
        "min=0\n",
        "room_Y = room_dimensions.iloc[:, 1] / Y_max\n",
        "room_scaled_Y = room_Y * (max - min) + min"
      ],
      "metadata": {
        "id": "pBrqWhBL9R0H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing the room dimensions Z-coordinates\n",
        "max=1\n",
        "min=0\n",
        "room_Z = room_dimensions.iloc[:, 2] / Z_max\n",
        "room_scaled_Z = room_Z * (max - min) + min"
      ],
      "metadata": {
        "id": "RT2ZwGQq9RU-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing the speaker dimensions X-coordinates\n",
        "max=1\n",
        "min=0\n",
        "speaker_X = speaker_coordinates.iloc[:, 0] / X_max\n",
        "speaker_scaled_X = speaker_X * (max - min) + min"
      ],
      "metadata": {
        "id": "9vPPNYfK-DEa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing the speaker dimensions Y-coordinates\n",
        "max=1\n",
        "min=0\n",
        "speaker_Y = speaker_coordinates.iloc[:, 1] / Y_max\n",
        "speaker_scaled_Y = speaker_Y * (max - min) + min"
      ],
      "metadata": {
        "id": "EYE1Mbwp-CkU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing the speaker dimensions Z-coordinates\n",
        "max=1\n",
        "min=0\n",
        "speaker_Z = speaker_coordinates.iloc[:, 2] / Z_max\n",
        "speaker_scaled_Z = speaker_Z * (max - min) + min"
      ],
      "metadata": {
        "id": "7XwkSZAl-Cba"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing the mic dimensions X-coordinates\n",
        "max=1\n",
        "min=0\n",
        "mic_X = mic_coordinates.iloc[:, 0] / X_max\n",
        "mic_scaled_X = mic_X * (max - min) + min"
      ],
      "metadata": {
        "id": "f_WA6OoV_BQ7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing the mic dimensions Y-coordinates\n",
        "max=1\n",
        "min=0\n",
        "mic_Y = mic_coordinates.iloc[:, 1] / Y_max\n",
        "mic_scaled_Y = mic_Y * (max - min) + min"
      ],
      "metadata": {
        "id": "IfLTRgFp_HXn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing the mic dimensions Z-coordinates\n",
        "max=1\n",
        "min=0\n",
        "mic_Z = mic_coordinates.iloc[:, 2] / Z_max\n",
        "mic_scaled_Z = mic_Z * (max - min) + min"
      ],
      "metadata": {
        "id": "PHDLiVGt_S3W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IGWoA2wbqX1d"
      },
      "outputs": [],
      "source": [
        "# Combine the scaled room dimensions, speaker coordinates, and mic coordinates\n",
        "normalized_data = pd.concat([room_scaled_X, room_scaled_Y, room_scaled_Z, speaker_scaled_X, speaker_scaled_Y, speaker_scaled_Z, mic_scaled_X, mic_scaled_Y, mic_scaled_Z], axis=1)  #dataframe concatenation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "sNvJ5Njjqgnw",
        "outputId": "1bbaed3e-2c57-4f7d-ec2b-5b7da250e2ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             0         1         2         3         4         5         6  \\\n",
              "0     0.604839  0.534884  0.704545  0.178397  0.238901  0.378099  0.182305   \n",
              "1     0.604839  0.534884  0.704545  0.178397  0.238901  0.378099  0.199292   \n",
              "2     0.604839  0.534884  0.704545  0.178397  0.238901  0.378099  0.173285   \n",
              "3     0.604839  0.534884  0.704545  0.178397  0.238901  0.378099  0.194989   \n",
              "4     0.604839  0.534884  0.704545  0.178397  0.238901  0.378099  0.181132   \n",
              "...        ...       ...       ...       ...       ...       ...       ...   \n",
              "4995  0.645161  0.697674  0.681818  0.203649  0.434578  0.452250  0.171799   \n",
              "4996  0.645161  0.697674  0.681818  0.203649  0.434578  0.452250  0.207393   \n",
              "4997  0.645161  0.697674  0.681818  0.203649  0.434578  0.452250  0.205911   \n",
              "4998  0.645161  0.697674  0.681818  0.203649  0.434578  0.452250  0.229879   \n",
              "4999  0.645161  0.697674  0.681818  0.203649  0.434578  0.452250  0.199822   \n",
              "\n",
              "             7         8  \n",
              "0     0.251240  0.372551  \n",
              "1     0.253242  0.350782  \n",
              "2     0.238197  0.433874  \n",
              "3     0.258975  0.388862  \n",
              "4     0.259444  0.344707  \n",
              "...        ...       ...  \n",
              "4995  0.475665  0.477451  \n",
              "4996  0.470757  0.408628  \n",
              "4997  0.390907  0.428510  \n",
              "4998  0.449306  0.472850  \n",
              "4999  0.396859  0.424264  \n",
              "\n",
              "[5000 rows x 9 columns]"
            ],
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              "\n",
              "  <div id=\"df-6096ee75-30aa-4bfb-87ed-1f6ef4dc0624\" class=\"colab-df-container\">\n",
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              "      <th>0</th>\n",
              "      <td>0.604839</td>\n",
              "      <td>0.534884</td>\n",
              "      <td>0.704545</td>\n",
              "      <td>0.178397</td>\n",
              "      <td>0.238901</td>\n",
              "      <td>0.378099</td>\n",
              "      <td>0.182305</td>\n",
              "      <td>0.251240</td>\n",
              "      <td>0.372551</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.604839</td>\n",
              "      <td>0.534884</td>\n",
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              "      <td>0.253242</td>\n",
              "      <td>0.350782</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.604839</td>\n",
              "      <td>0.534884</td>\n",
              "      <td>0.704545</td>\n",
              "      <td>0.178397</td>\n",
              "      <td>0.238901</td>\n",
              "      <td>0.378099</td>\n",
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              "      <td>0.238197</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.604839</td>\n",
              "      <td>0.534884</td>\n",
              "      <td>0.704545</td>\n",
              "      <td>0.178397</td>\n",
              "      <td>0.238901</td>\n",
              "      <td>0.378099</td>\n",
              "      <td>0.194989</td>\n",
              "      <td>0.258975</td>\n",
              "      <td>0.388862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.604839</td>\n",
              "      <td>0.534884</td>\n",
              "      <td>0.704545</td>\n",
              "      <td>0.178397</td>\n",
              "      <td>0.238901</td>\n",
              "      <td>0.378099</td>\n",
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              "      <td>0.259444</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.697674</td>\n",
              "      <td>0.681818</td>\n",
              "      <td>0.203649</td>\n",
              "      <td>0.434578</td>\n",
              "      <td>0.452250</td>\n",
              "      <td>0.171799</td>\n",
              "      <td>0.475665</td>\n",
              "      <td>0.477451</td>\n",
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              "      <td>0.203649</td>\n",
              "      <td>0.434578</td>\n",
              "      <td>0.452250</td>\n",
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              "      <td>0.470757</td>\n",
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              "      <td>0.434578</td>\n",
              "      <td>0.452250</td>\n",
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              "      <td>0.390907</td>\n",
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              "      <th>4998</th>\n",
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              "      <td>0.697674</td>\n",
              "      <td>0.681818</td>\n",
              "      <td>0.203649</td>\n",
              "      <td>0.434578</td>\n",
              "      <td>0.452250</td>\n",
              "      <td>0.229879</td>\n",
              "      <td>0.449306</td>\n",
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              "      <td>0.203649</td>\n",
              "      <td>0.434578</td>\n",
              "      <td>0.452250</td>\n",
              "      <td>0.199822</td>\n",
              "      <td>0.396859</td>\n",
              "      <td>0.424264</td>\n",
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              "    height: 32px;\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_2d6dff87-f7a8-475f-b91b-dd9acd90271d\">\n",
              "    <style>\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('normalized_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
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              "        document.querySelector('#id_2d6dff87-f7a8-475f-b91b-dd9acd90271d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('normalized_data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "normalized_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-hoAAKIqktK",
        "outputId": "c846280d-5ebe-49cb-f9b7-e14f3d4a7df6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3000\n",
            "2000\n"
          ]
        }
      ],
      "source": [
        "total_rows = len(data)\n",
        "train_rows = int(0.6 * total_rows)\n",
        "test_rows = total_rows - train_rows\n",
        "\n",
        "print(train_rows)\n",
        "print(test_rows)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eXzrT_z9qvef"
      },
      "outputs": [],
      "source": [
        "# Split the DataFrame into training and testing sets\n",
        "train_data = normalized_data.iloc[:train_rows, :]\n",
        "test_data = normalized_data.iloc[train_rows:, :]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "rwpjeAG6rJUP",
        "outputId": "e7d70152-b2fd-4ed2-d55a-da27902fc11a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             0         1         2         3         4         5         6  \\\n",
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              "\n",
              "[2000 rows x 9 columns]"
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              "      <td>0.434578</td>\n",
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              "<p>2000 rows × 9 columns</p>\n",
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              "\n",
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              "\n",
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              "\n",
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              "        document.querySelector('#df-a752bc35-adcb-44c6-a0d3-82195ce52f02 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-401401db-77c8-4480-acc2-6c6e09c29536')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-401401db-77c8-4480-acc2-6c6e09c29536 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_50abf1b1-afd6-4339-910e-8dcd94a8e1af\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_50abf1b1-afd6-4339-910e-8dcd94a8e1af button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test_data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "test_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "ApAzpSeArM9E",
        "outputId": "2ddfef78-7e76-4e3f-8826-28149c9eb061"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             0         1         2         3         4         5         6  \\\n",
              "0     0.435484  0.627907  0.681818  0.260508  0.359173  0.452250  0.268511   \n",
              "1     0.620968  0.488372  0.750000  0.372271  0.233263  0.401515  0.390607   \n",
              "2     0.435484  0.627907  0.681818  0.198925  0.321353  0.424702  0.198925   \n",
              "3     0.451613  0.465116  0.704545  0.173021  0.232558  0.316116  0.194841   \n",
              "4     0.419355  0.488372  0.727273  0.217009  0.241250  0.371901  0.216347   \n",
              "...        ...       ...       ...       ...       ...       ...       ...   \n",
              "2995  0.741935  0.802326  0.863636  0.203649  0.481207  0.423783  0.220242   \n",
              "2996  0.620968  0.488372  0.750000  0.224585  0.253700  0.383838  0.192734   \n",
              "2997  0.620968  0.488372  0.750000  0.218557  0.237961  0.454545  0.219706   \n",
              "2998  0.927419  0.860465  0.954545  0.723363  0.564012  0.494490  0.714482   \n",
              "2999  0.846774  0.941860  0.863636  0.478983  0.372210  0.428375  0.488889   \n",
              "\n",
              "             7         8  \n",
              "0     0.341098  0.390055  \n",
              "1     0.179134  0.456338  \n",
              "2     0.321353  0.543388  \n",
              "3     0.163667  0.275190  \n",
              "4     0.245813  0.351080  \n",
              "...        ...       ...  \n",
              "2995  0.501282  0.434547  \n",
              "2996  0.294787  0.409040  \n",
              "2997  0.235290  0.357863  \n",
              "2998  0.633506  0.763446  \n",
              "2999  0.334824  0.414582  \n",
              "\n",
              "[3000 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b12761ec-dfee-46e3-bd7f-6cc30ca1f8fe\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.435484</td>\n",
              "      <td>0.627907</td>\n",
              "      <td>0.681818</td>\n",
              "      <td>0.260508</td>\n",
              "      <td>0.359173</td>\n",
              "      <td>0.452250</td>\n",
              "      <td>0.268511</td>\n",
              "      <td>0.341098</td>\n",
              "      <td>0.390055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.620968</td>\n",
              "      <td>0.488372</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.372271</td>\n",
              "      <td>0.233263</td>\n",
              "      <td>0.401515</td>\n",
              "      <td>0.390607</td>\n",
              "      <td>0.179134</td>\n",
              "      <td>0.456338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.435484</td>\n",
              "      <td>0.627907</td>\n",
              "      <td>0.681818</td>\n",
              "      <td>0.198925</td>\n",
              "      <td>0.321353</td>\n",
              "      <td>0.424702</td>\n",
              "      <td>0.198925</td>\n",
              "      <td>0.321353</td>\n",
              "      <td>0.543388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.451613</td>\n",
              "      <td>0.465116</td>\n",
              "      <td>0.704545</td>\n",
              "      <td>0.173021</td>\n",
              "      <td>0.232558</td>\n",
              "      <td>0.316116</td>\n",
              "      <td>0.194841</td>\n",
              "      <td>0.163667</td>\n",
              "      <td>0.275190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.419355</td>\n",
              "      <td>0.488372</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>0.217009</td>\n",
              "      <td>0.241250</td>\n",
              "      <td>0.371901</td>\n",
              "      <td>0.216347</td>\n",
              "      <td>0.245813</td>\n",
              "      <td>0.351080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>0.741935</td>\n",
              "      <td>0.802326</td>\n",
              "      <td>0.863636</td>\n",
              "      <td>0.203649</td>\n",
              "      <td>0.481207</td>\n",
              "      <td>0.423783</td>\n",
              "      <td>0.220242</td>\n",
              "      <td>0.501282</td>\n",
              "      <td>0.434547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>0.620968</td>\n",
              "      <td>0.488372</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.224585</td>\n",
              "      <td>0.253700</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>0.192734</td>\n",
              "      <td>0.294787</td>\n",
              "      <td>0.409040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>0.620968</td>\n",
              "      <td>0.488372</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.218557</td>\n",
              "      <td>0.237961</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.219706</td>\n",
              "      <td>0.235290</td>\n",
              "      <td>0.357863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>0.927419</td>\n",
              "      <td>0.860465</td>\n",
              "      <td>0.954545</td>\n",
              "      <td>0.723363</td>\n",
              "      <td>0.564012</td>\n",
              "      <td>0.494490</td>\n",
              "      <td>0.714482</td>\n",
              "      <td>0.633506</td>\n",
              "      <td>0.763446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>0.846774</td>\n",
              "      <td>0.941860</td>\n",
              "      <td>0.863636</td>\n",
              "      <td>0.478983</td>\n",
              "      <td>0.372210</td>\n",
              "      <td>0.428375</td>\n",
              "      <td>0.488889</td>\n",
              "      <td>0.334824</td>\n",
              "      <td>0.414582</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b12761ec-dfee-46e3-bd7f-6cc30ca1f8fe')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-b12761ec-dfee-46e3-bd7f-6cc30ca1f8fe button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b12761ec-dfee-46e3-bd7f-6cc30ca1f8fe');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# Shuffle the train dataset\n",
        "train_data_shuffled = train_data.sample(frac=1, random_state=42).reset_index(drop=True)  # frac=1 shuffles the entire dataset, random_state for reproducibility\n",
        "train_data_shuffled"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "C3EYu6LUrk_D",
        "outputId": "f6ec1e85-5afa-4c17-8642-e27556546241"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             0         1         2         3         4         5         6  \\\n",
              "0     0.645161  0.697674  0.681818  0.412186  0.312427  0.369605  0.399477   \n",
              "1     0.830645  0.744186  0.795455  0.582111  0.260747  0.345500  0.589116   \n",
              "2     0.419355  0.383721  0.659091  0.253177  0.208715  0.343434  0.245147   \n",
              "3     0.604839  0.604651  0.750000  0.389378  0.350951  0.377410  0.388715   \n",
              "4     0.419355  0.383721  0.659091  0.194526  0.213648  0.386364  0.207453   \n",
              "...        ...       ...       ...       ...       ...       ...       ...   \n",
              "1995  0.556452  0.546512  0.727273  0.380987  0.293399  0.375574  0.350568   \n",
              "1996  0.419355  0.383721  0.659091  0.213099  0.156096  0.209596  0.243818   \n",
              "1997  0.604839  0.604651  0.750000  0.375122  0.334038  0.374197  0.348422   \n",
              "1998  0.419355  0.383721  0.659091  0.254154  0.153629  0.277778  0.264372   \n",
              "1999  0.556452  0.546512  0.727273  0.262871  0.268734  0.342516  0.255051   \n",
              "\n",
              "             7         8  \n",
              "0     0.338238  0.396795  \n",
              "1     0.262036  0.327120  \n",
              "2     0.198219  0.399022  \n",
              "3     0.355515  0.356590  \n",
              "4     0.176992  0.503157  \n",
              "...        ...       ...  \n",
              "1995  0.261122  0.429050  \n",
              "1996  0.167263  0.205609  \n",
              "1997  0.368581  0.232150  \n",
              "1998  0.145433  0.177449  \n",
              "1999  0.267657  0.314365  \n",
              "\n",
              "[2000 rows x 9 columns]"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
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              "      <th>8</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.645161</td>\n",
              "      <td>0.697674</td>\n",
              "      <td>0.681818</td>\n",
              "      <td>0.412186</td>\n",
              "      <td>0.312427</td>\n",
              "      <td>0.369605</td>\n",
              "      <td>0.399477</td>\n",
              "      <td>0.338238</td>\n",
              "      <td>0.396795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.830645</td>\n",
              "      <td>0.744186</td>\n",
              "      <td>0.795455</td>\n",
              "      <td>0.582111</td>\n",
              "      <td>0.260747</td>\n",
              "      <td>0.345500</td>\n",
              "      <td>0.589116</td>\n",
              "      <td>0.262036</td>\n",
              "      <td>0.327120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.419355</td>\n",
              "      <td>0.383721</td>\n",
              "      <td>0.659091</td>\n",
              "      <td>0.253177</td>\n",
              "      <td>0.208715</td>\n",
              "      <td>0.343434</td>\n",
              "      <td>0.245147</td>\n",
              "      <td>0.198219</td>\n",
              "      <td>0.399022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.604839</td>\n",
              "      <td>0.604651</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.389378</td>\n",
              "      <td>0.350951</td>\n",
              "      <td>0.377410</td>\n",
              "      <td>0.388715</td>\n",
              "      <td>0.355515</td>\n",
              "      <td>0.356590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.419355</td>\n",
              "      <td>0.383721</td>\n",
              "      <td>0.659091</td>\n",
              "      <td>0.194526</td>\n",
              "      <td>0.213648</td>\n",
              "      <td>0.386364</td>\n",
              "      <td>0.207453</td>\n",
              "      <td>0.176992</td>\n",
              "      <td>0.503157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>0.556452</td>\n",
              "      <td>0.546512</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>0.380987</td>\n",
              "      <td>0.293399</td>\n",
              "      <td>0.375574</td>\n",
              "      <td>0.350568</td>\n",
              "      <td>0.261122</td>\n",
              "      <td>0.429050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>0.419355</td>\n",
              "      <td>0.383721</td>\n",
              "      <td>0.659091</td>\n",
              "      <td>0.213099</td>\n",
              "      <td>0.156096</td>\n",
              "      <td>0.209596</td>\n",
              "      <td>0.243818</td>\n",
              "      <td>0.167263</td>\n",
              "      <td>0.205609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>0.604839</td>\n",
              "      <td>0.604651</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.375122</td>\n",
              "      <td>0.334038</td>\n",
              "      <td>0.374197</td>\n",
              "      <td>0.348422</td>\n",
              "      <td>0.368581</td>\n",
              "      <td>0.232150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>0.419355</td>\n",
              "      <td>0.383721</td>\n",
              "      <td>0.659091</td>\n",
              "      <td>0.254154</td>\n",
              "      <td>0.153629</td>\n",
              "      <td>0.277778</td>\n",
              "      <td>0.264372</td>\n",
              "      <td>0.145433</td>\n",
              "      <td>0.177449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>0.556452</td>\n",
              "      <td>0.546512</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>0.262871</td>\n",
              "      <td>0.268734</td>\n",
              "      <td>0.342516</td>\n",
              "      <td>0.255051</td>\n",
              "      <td>0.267657</td>\n",
              "      <td>0.314365</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2000 rows × 9 columns</p>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-25e95536-f501-4c16-b11f-7b455e805bef\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-25e95536-f501-4c16-b11f-7b455e805bef')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-25e95536-f501-4c16-b11f-7b455e805bef button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_c4c56809-4080-4a38-9c69-ff5c8e467013\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test_data_shuffled')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_c4c56809-4080-4a38-9c69-ff5c8e467013 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test_data_shuffled');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Shuffle the test dataset\n",
        "test_data_shuffled = test_data.sample(frac=1, random_state=42).reset_index(drop=True)  # frac=1 shuffles the entire dataset, random_state for reproducibility\n",
        "test_data_shuffled"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1NiUwE2trrcL"
      },
      "outputs": [],
      "source": [
        "# Separate the features (X) and target labels (y) for both training and testing sets\n",
        "X_train = train_data_shuffled.iloc[:,:-3]   #features\n",
        "y_train = train_data_shuffled.iloc[:, -3:]   #features\n",
        "# convert all values into float as expected by neural networks model\n",
        "X_train, y_train = X_train.astype('float'), y_train.astype('float')\n",
        "\n",
        "X_test = test_data_shuffled.iloc[:,:-3]    #labels\n",
        "y_test = test_data_shuffled.iloc[:, -3:]   #labels\n",
        "# convert all values into float as expected by neural networks model\n",
        "X_test, y_test = X_test.astype('float'), y_test.astype('float')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OyFIqPtKsGPR",
        "outputId": "960830d0-833d-4af3-a10a-d50bf9d11383"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (3000, 6)\n",
            "y_train shape: (3000, 3)\n",
            "X_test shape: (2000, 6)\n",
            "y_test shape: (2000, 3)\n"
          ]
        }
      ],
      "source": [
        "print(\"X_train shape:\", X_train.shape)\n",
        "print(\"y_train shape:\", y_train.shape)\n",
        "print(\"X_test shape:\", X_test.shape)\n",
        "print(\"y_test shape:\", y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0AbFVk3LsJ6Q"
      },
      "outputs": [],
      "source": [
        "from  keras.models import Sequential\n",
        "from keras.layers import Dense"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7uOiBXIusN9t"
      },
      "outputs": [],
      "source": [
        "# Training the neural network model\n",
        "\n",
        "# Model Construction - layer by layer\n",
        "# define the keras model\n",
        "#no of layers = depth; no. of neurons = length\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(20, input_shape=(6, ), activation='relu', kernel_initializer='he_normal'))   #dense-fully connected\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Dense(15, activation='relu', kernel_initializer='he_normal'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(10, activation='relu', kernel_initializer='he_normal'))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(6, activation='relu', kernel_initializer='he_normal'))\n",
        "model.add(Dropout(0.4))\n",
        "# model.add(Dense(10, activation='relu', kernel_initializer='he_normal'))\n",
        "model.add(Dense(3, activation='relu'))  # Output layer with 3 nodes for mic coordinates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Dy7DhUcsR9B",
        "outputId": "24c4591d-a45f-4df1-9bcb-0359f17b1c27"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 20)                140       \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 20)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 15)                315       \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 15)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                160       \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 10)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 6)                 66        \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 6)                 0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 3)                 21        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 702 (2.74 KB)\n",
            "Trainable params: 702 (2.74 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Compile model\n",
        "\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mA0hqsM2sVd6",
        "outputId": "67a89dea-1c64-4711-8500-266684a4d47b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/130\n",
            "94/94 - 2s - loss: 0.0898 - 2s/epoch - 16ms/step\n",
            "Epoch 2/130\n",
            "94/94 - 0s - loss: 0.0403 - 243ms/epoch - 3ms/step\n",
            "Epoch 3/130\n",
            "94/94 - 0s - loss: 0.0182 - 213ms/epoch - 2ms/step\n",
            "Epoch 4/130\n",
            "94/94 - 0s - loss: 0.0143 - 209ms/epoch - 2ms/step\n",
            "Epoch 5/130\n",
            "94/94 - 0s - loss: 0.0128 - 212ms/epoch - 2ms/step\n",
            "Epoch 6/130\n",
            "94/94 - 0s - loss: 0.0118 - 220ms/epoch - 2ms/step\n",
            "Epoch 7/130\n",
            "94/94 - 0s - loss: 0.0111 - 216ms/epoch - 2ms/step\n",
            "Epoch 8/130\n",
            "94/94 - 0s - loss: 0.0106 - 207ms/epoch - 2ms/step\n",
            "Epoch 9/130\n",
            "94/94 - 0s - loss: 0.0105 - 198ms/epoch - 2ms/step\n",
            "Epoch 10/130\n",
            "94/94 - 0s - loss: 0.0105 - 214ms/epoch - 2ms/step\n",
            "Epoch 11/130\n",
            "94/94 - 0s - loss: 0.0103 - 238ms/epoch - 3ms/step\n",
            "Epoch 12/130\n",
            "94/94 - 0s - loss: 0.0102 - 230ms/epoch - 2ms/step\n",
            "Epoch 13/130\n",
            "94/94 - 0s - loss: 0.0100 - 237ms/epoch - 3ms/step\n",
            "Epoch 14/130\n",
            "94/94 - 0s - loss: 0.0100 - 229ms/epoch - 2ms/step\n",
            "Epoch 15/130\n",
            "94/94 - 0s - loss: 0.0097 - 246ms/epoch - 3ms/step\n",
            "Epoch 16/130\n",
            "94/94 - 0s - loss: 0.0098 - 204ms/epoch - 2ms/step\n",
            "Epoch 17/130\n",
            "94/94 - 0s - loss: 0.0098 - 162ms/epoch - 2ms/step\n",
            "Epoch 18/130\n",
            "94/94 - 0s - loss: 0.0097 - 149ms/epoch - 2ms/step\n",
            "Epoch 19/130\n",
            "94/94 - 0s - loss: 0.0097 - 152ms/epoch - 2ms/step\n",
            "Epoch 20/130\n",
            "94/94 - 0s - loss: 0.0096 - 159ms/epoch - 2ms/step\n",
            "Epoch 21/130\n",
            "94/94 - 0s - loss: 0.0094 - 165ms/epoch - 2ms/step\n",
            "Epoch 22/130\n",
            "94/94 - 0s - loss: 0.0095 - 149ms/epoch - 2ms/step\n",
            "Epoch 23/130\n",
            "94/94 - 0s - loss: 0.0094 - 153ms/epoch - 2ms/step\n",
            "Epoch 24/130\n",
            "94/94 - 0s - loss: 0.0095 - 152ms/epoch - 2ms/step\n",
            "Epoch 25/130\n",
            "94/94 - 0s - loss: 0.0094 - 143ms/epoch - 2ms/step\n",
            "Epoch 26/130\n",
            "94/94 - 0s - loss: 0.0093 - 151ms/epoch - 2ms/step\n",
            "Epoch 27/130\n",
            "94/94 - 0s - loss: 0.0094 - 148ms/epoch - 2ms/step\n",
            "Epoch 28/130\n",
            "94/94 - 0s - loss: 0.0095 - 164ms/epoch - 2ms/step\n",
            "Epoch 29/130\n",
            "94/94 - 0s - loss: 0.0092 - 162ms/epoch - 2ms/step\n",
            "Epoch 30/130\n",
            "94/94 - 0s - loss: 0.0090 - 151ms/epoch - 2ms/step\n",
            "Epoch 31/130\n",
            "94/94 - 0s - loss: 0.0092 - 153ms/epoch - 2ms/step\n",
            "Epoch 32/130\n",
            "94/94 - 0s - loss: 0.0091 - 156ms/epoch - 2ms/step\n",
            "Epoch 33/130\n",
            "94/94 - 0s - loss: 0.0090 - 154ms/epoch - 2ms/step\n",
            "Epoch 34/130\n",
            "94/94 - 0s - loss: 0.0092 - 164ms/epoch - 2ms/step\n",
            "Epoch 35/130\n",
            "94/94 - 0s - loss: 0.0093 - 157ms/epoch - 2ms/step\n",
            "Epoch 36/130\n",
            "94/94 - 0s - loss: 0.0090 - 148ms/epoch - 2ms/step\n",
            "Epoch 37/130\n",
            "94/94 - 0s - loss: 0.0092 - 141ms/epoch - 1ms/step\n",
            "Epoch 38/130\n",
            "94/94 - 0s - loss: 0.0090 - 149ms/epoch - 2ms/step\n",
            "Epoch 39/130\n",
            "94/94 - 0s - loss: 0.0092 - 152ms/epoch - 2ms/step\n",
            "Epoch 40/130\n",
            "94/94 - 0s - loss: 0.0090 - 156ms/epoch - 2ms/step\n",
            "Epoch 41/130\n",
            "94/94 - 0s - loss: 0.0088 - 157ms/epoch - 2ms/step\n",
            "Epoch 42/130\n",
            "94/94 - 0s - loss: 0.0091 - 153ms/epoch - 2ms/step\n",
            "Epoch 43/130\n",
            "94/94 - 0s - loss: 0.0089 - 149ms/epoch - 2ms/step\n",
            "Epoch 44/130\n",
            "94/94 - 0s - loss: 0.0093 - 149ms/epoch - 2ms/step\n",
            "Epoch 45/130\n",
            "94/94 - 0s - loss: 0.0088 - 147ms/epoch - 2ms/step\n",
            "Epoch 46/130\n",
            "94/94 - 0s - loss: 0.0090 - 147ms/epoch - 2ms/step\n",
            "Epoch 47/130\n",
            "94/94 - 0s - loss: 0.0089 - 163ms/epoch - 2ms/step\n",
            "Epoch 48/130\n",
            "94/94 - 0s - loss: 0.0088 - 167ms/epoch - 2ms/step\n",
            "Epoch 49/130\n",
            "94/94 - 0s - loss: 0.0087 - 152ms/epoch - 2ms/step\n",
            "Epoch 50/130\n",
            "94/94 - 0s - loss: 0.0087 - 147ms/epoch - 2ms/step\n",
            "Epoch 51/130\n",
            "94/94 - 0s - loss: 0.0089 - 152ms/epoch - 2ms/step\n",
            "Epoch 52/130\n",
            "94/94 - 0s - loss: 0.0087 - 156ms/epoch - 2ms/step\n",
            "Epoch 53/130\n",
            "94/94 - 0s - loss: 0.0087 - 148ms/epoch - 2ms/step\n",
            "Epoch 54/130\n",
            "94/94 - 0s - loss: 0.0087 - 159ms/epoch - 2ms/step\n",
            "Epoch 55/130\n",
            "94/94 - 0s - loss: 0.0088 - 151ms/epoch - 2ms/step\n",
            "Epoch 56/130\n",
            "94/94 - 0s - loss: 0.0089 - 145ms/epoch - 2ms/step\n",
            "Epoch 57/130\n",
            "94/94 - 0s - loss: 0.0088 - 140ms/epoch - 1ms/step\n",
            "Epoch 58/130\n",
            "94/94 - 0s - loss: 0.0085 - 149ms/epoch - 2ms/step\n",
            "Epoch 59/130\n",
            "94/94 - 0s - loss: 0.0088 - 146ms/epoch - 2ms/step\n",
            "Epoch 60/130\n",
            "94/94 - 0s - loss: 0.0088 - 152ms/epoch - 2ms/step\n",
            "Epoch 61/130\n",
            "94/94 - 0s - loss: 0.0086 - 160ms/epoch - 2ms/step\n",
            "Epoch 62/130\n",
            "94/94 - 0s - loss: 0.0084 - 156ms/epoch - 2ms/step\n",
            "Epoch 63/130\n",
            "94/94 - 0s - loss: 0.0087 - 148ms/epoch - 2ms/step\n",
            "Epoch 64/130\n",
            "94/94 - 0s - loss: 0.0087 - 158ms/epoch - 2ms/step\n",
            "Epoch 65/130\n",
            "94/94 - 0s - loss: 0.0084 - 157ms/epoch - 2ms/step\n",
            "Epoch 66/130\n",
            "94/94 - 0s - loss: 0.0087 - 151ms/epoch - 2ms/step\n",
            "Epoch 67/130\n",
            "94/94 - 0s - loss: 0.0087 - 167ms/epoch - 2ms/step\n",
            "Epoch 68/130\n",
            "94/94 - 0s - loss: 0.0086 - 154ms/epoch - 2ms/step\n",
            "Epoch 69/130\n",
            "94/94 - 0s - loss: 0.0084 - 152ms/epoch - 2ms/step\n",
            "Epoch 70/130\n",
            "94/94 - 0s - loss: 0.0086 - 145ms/epoch - 2ms/step\n",
            "Epoch 71/130\n",
            "94/94 - 0s - loss: 0.0086 - 156ms/epoch - 2ms/step\n",
            "Epoch 72/130\n",
            "94/94 - 0s - loss: 0.0086 - 149ms/epoch - 2ms/step\n",
            "Epoch 73/130\n",
            "94/94 - 0s - loss: 0.0084 - 149ms/epoch - 2ms/step\n",
            "Epoch 74/130\n",
            "94/94 - 0s - loss: 0.0086 - 161ms/epoch - 2ms/step\n",
            "Epoch 75/130\n",
            "94/94 - 0s - loss: 0.0085 - 159ms/epoch - 2ms/step\n",
            "Epoch 76/130\n",
            "94/94 - 0s - loss: 0.0085 - 152ms/epoch - 2ms/step\n",
            "Epoch 77/130\n",
            "94/94 - 0s - loss: 0.0086 - 162ms/epoch - 2ms/step\n",
            "Epoch 78/130\n",
            "94/94 - 0s - loss: 0.0085 - 157ms/epoch - 2ms/step\n",
            "Epoch 79/130\n",
            "94/94 - 0s - loss: 0.0084 - 151ms/epoch - 2ms/step\n",
            "Epoch 80/130\n",
            "94/94 - 0s - loss: 0.0086 - 212ms/epoch - 2ms/step\n",
            "Epoch 81/130\n",
            "94/94 - 0s - loss: 0.0085 - 236ms/epoch - 3ms/step\n",
            "Epoch 82/130\n",
            "94/94 - 0s - loss: 0.0086 - 232ms/epoch - 2ms/step\n",
            "Epoch 83/130\n",
            "94/94 - 0s - loss: 0.0086 - 222ms/epoch - 2ms/step\n",
            "Epoch 84/130\n",
            "94/94 - 0s - loss: 0.0086 - 256ms/epoch - 3ms/step\n",
            "Epoch 85/130\n",
            "94/94 - 0s - loss: 0.0083 - 261ms/epoch - 3ms/step\n",
            "Epoch 86/130\n",
            "94/94 - 0s - loss: 0.0085 - 262ms/epoch - 3ms/step\n",
            "Epoch 87/130\n",
            "94/94 - 0s - loss: 0.0083 - 253ms/epoch - 3ms/step\n",
            "Epoch 88/130\n",
            "94/94 - 0s - loss: 0.0084 - 283ms/epoch - 3ms/step\n",
            "Epoch 89/130\n",
            "94/94 - 0s - loss: 0.0085 - 222ms/epoch - 2ms/step\n",
            "Epoch 90/130\n",
            "94/94 - 0s - loss: 0.0084 - 239ms/epoch - 3ms/step\n",
            "Epoch 91/130\n",
            "94/94 - 0s - loss: 0.0084 - 225ms/epoch - 2ms/step\n",
            "Epoch 92/130\n",
            "94/94 - 0s - loss: 0.0084 - 237ms/epoch - 3ms/step\n",
            "Epoch 93/130\n",
            "94/94 - 0s - loss: 0.0084 - 219ms/epoch - 2ms/step\n",
            "Epoch 94/130\n",
            "94/94 - 0s - loss: 0.0083 - 149ms/epoch - 2ms/step\n",
            "Epoch 95/130\n",
            "94/94 - 0s - loss: 0.0084 - 147ms/epoch - 2ms/step\n",
            "Epoch 96/130\n",
            "94/94 - 0s - loss: 0.0081 - 143ms/epoch - 2ms/step\n",
            "Epoch 97/130\n",
            "94/94 - 0s - loss: 0.0086 - 150ms/epoch - 2ms/step\n",
            "Epoch 98/130\n",
            "94/94 - 0s - loss: 0.0084 - 155ms/epoch - 2ms/step\n",
            "Epoch 99/130\n",
            "94/94 - 0s - loss: 0.0083 - 152ms/epoch - 2ms/step\n",
            "Epoch 100/130\n",
            "94/94 - 0s - loss: 0.0082 - 160ms/epoch - 2ms/step\n",
            "Epoch 101/130\n",
            "94/94 - 0s - loss: 0.0084 - 145ms/epoch - 2ms/step\n",
            "Epoch 102/130\n",
            "94/94 - 0s - loss: 0.0085 - 143ms/epoch - 2ms/step\n",
            "Epoch 103/130\n",
            "94/94 - 0s - loss: 0.0084 - 155ms/epoch - 2ms/step\n",
            "Epoch 104/130\n",
            "94/94 - 0s - loss: 0.0086 - 151ms/epoch - 2ms/step\n",
            "Epoch 105/130\n",
            "94/94 - 0s - loss: 0.0083 - 156ms/epoch - 2ms/step\n",
            "Epoch 106/130\n",
            "94/94 - 0s - loss: 0.0083 - 162ms/epoch - 2ms/step\n",
            "Epoch 107/130\n",
            "94/94 - 0s - loss: 0.0083 - 151ms/epoch - 2ms/step\n",
            "Epoch 108/130\n",
            "94/94 - 0s - loss: 0.0085 - 155ms/epoch - 2ms/step\n",
            "Epoch 109/130\n",
            "94/94 - 0s - loss: 0.0083 - 154ms/epoch - 2ms/step\n",
            "Epoch 110/130\n",
            "94/94 - 0s - loss: 0.0082 - 153ms/epoch - 2ms/step\n",
            "Epoch 111/130\n",
            "94/94 - 0s - loss: 0.0084 - 153ms/epoch - 2ms/step\n",
            "Epoch 112/130\n",
            "94/94 - 0s - loss: 0.0085 - 147ms/epoch - 2ms/step\n",
            "Epoch 113/130\n",
            "94/94 - 0s - loss: 0.0083 - 158ms/epoch - 2ms/step\n",
            "Epoch 114/130\n",
            "94/94 - 0s - loss: 0.0083 - 150ms/epoch - 2ms/step\n",
            "Epoch 115/130\n",
            "94/94 - 0s - loss: 0.0083 - 145ms/epoch - 2ms/step\n",
            "Epoch 116/130\n",
            "94/94 - 0s - loss: 0.0085 - 148ms/epoch - 2ms/step\n",
            "Epoch 117/130\n",
            "94/94 - 0s - loss: 0.0085 - 152ms/epoch - 2ms/step\n",
            "Epoch 118/130\n",
            "94/94 - 0s - loss: 0.0081 - 150ms/epoch - 2ms/step\n",
            "Epoch 119/130\n",
            "94/94 - 0s - loss: 0.0085 - 155ms/epoch - 2ms/step\n",
            "Epoch 120/130\n",
            "94/94 - 0s - loss: 0.0082 - 151ms/epoch - 2ms/step\n",
            "Epoch 121/130\n",
            "94/94 - 0s - loss: 0.0082 - 143ms/epoch - 2ms/step\n",
            "Epoch 122/130\n",
            "94/94 - 0s - loss: 0.0084 - 145ms/epoch - 2ms/step\n",
            "Epoch 123/130\n",
            "94/94 - 0s - loss: 0.0083 - 153ms/epoch - 2ms/step\n",
            "Epoch 124/130\n",
            "94/94 - 0s - loss: 0.0085 - 165ms/epoch - 2ms/step\n",
            "Epoch 125/130\n",
            "94/94 - 0s - loss: 0.0085 - 153ms/epoch - 2ms/step\n",
            "Epoch 126/130\n",
            "94/94 - 0s - loss: 0.0083 - 178ms/epoch - 2ms/step\n",
            "Epoch 127/130\n",
            "94/94 - 0s - loss: 0.0084 - 183ms/epoch - 2ms/step\n",
            "Epoch 128/130\n",
            "94/94 - 0s - loss: 0.0082 - 151ms/epoch - 2ms/step\n",
            "Epoch 129/130\n",
            "94/94 - 0s - loss: 0.0084 - 153ms/epoch - 2ms/step\n",
            "Epoch 130/130\n",
            "94/94 - 0s - loss: 0.0081 - 151ms/epoch - 2ms/step\n"
          ]
        }
      ],
      "source": [
        "# fit the keras model on the dataset\n",
        "model1 = model.fit(X_train, y_train, epochs=130, batch_size=32, verbose=2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(model1.history['loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "H8EkYjvbzoO2",
        "outputId": "b61b0fcf-c7a1-4a25-e1af-e2b6ed8f47b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x786b0806d270>]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jhOsoImlsbfF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "48cc69eb-1604-45da-800a-5e3cb1d851ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             6         7         8\n",
              "0     0.399477  0.338238  0.396795\n",
              "1     0.589116  0.262036  0.327120\n",
              "2     0.245147  0.198219  0.399022\n",
              "3     0.388715  0.355515  0.356590\n",
              "4     0.207453  0.176992  0.503157\n",
              "...        ...       ...       ...\n",
              "1995  0.350568  0.261122  0.429050\n",
              "1996  0.243818  0.167263  0.205609\n",
              "1997  0.348422  0.368581  0.232150\n",
              "1998  0.264372  0.145433  0.177449\n",
              "1999  0.255051  0.267657  0.314365\n",
              "\n",
              "[2000 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-64cb8e10-2411-4d44-bcd0-0971ecff5421\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.399477</td>\n",
              "      <td>0.338238</td>\n",
              "      <td>0.396795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.589116</td>\n",
              "      <td>0.262036</td>\n",
              "      <td>0.327120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.245147</td>\n",
              "      <td>0.198219</td>\n",
              "      <td>0.399022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.388715</td>\n",
              "      <td>0.355515</td>\n",
              "      <td>0.356590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.207453</td>\n",
              "      <td>0.176992</td>\n",
              "      <td>0.503157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>0.350568</td>\n",
              "      <td>0.261122</td>\n",
              "      <td>0.429050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>0.243818</td>\n",
              "      <td>0.167263</td>\n",
              "      <td>0.205609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>0.348422</td>\n",
              "      <td>0.368581</td>\n",
              "      <td>0.232150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>0.264372</td>\n",
              "      <td>0.145433</td>\n",
              "      <td>0.177449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>0.255051</td>\n",
              "      <td>0.267657</td>\n",
              "      <td>0.314365</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2000 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64cb8e10-2411-4d44-bcd0-0971ecff5421')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-64cb8e10-2411-4d44-bcd0-0971ecff5421 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-64cb8e10-2411-4d44-bcd0-0971ecff5421');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2ded40db-59e6-4412-9249-0d0de47df495\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2ded40db-59e6-4412-9249-0d0de47df495')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2ded40db-59e6-4412-9249-0d0de47df495 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_617ac27d-8468-4db2-b4bd-b953100227e9\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('y_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_617ac27d-8468-4db2-b4bd-b953100227e9 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('y_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7A5b29mvFtF",
        "outputId": "15892940-0308-47d5-ca87-7a5e9ab9d0dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = X_test.drop_duplicates()"
      ],
      "metadata": {
        "id": "6ma4clgcvLTm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "PNREI_UAvWA5",
        "outputId": "bb77bea7-ee94-4724-c439-cc7b150cd543"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             0         1         2         3         4         5\n",
              "0     0.645161  0.697674  0.681818  0.412186  0.312427  0.369605\n",
              "1     0.830645  0.744186  0.795455  0.582111  0.260747  0.345500\n",
              "2     0.419355  0.383721  0.659091  0.253177  0.208715  0.343434\n",
              "3     0.604839  0.604651  0.750000  0.389378  0.350951  0.377410\n",
              "4     0.419355  0.383721  0.659091  0.194526  0.213648  0.386364\n",
              "...        ...       ...       ...       ...       ...       ...\n",
              "1201  0.604839  0.604651  0.750000  0.443548  0.266385  0.337236\n",
              "1227  0.604839  0.604651  0.750000  0.161290  0.325581  0.345271\n",
              "1294  0.645161  0.697674  0.681818  0.330727  0.368804  0.383379\n",
              "1319  0.709677  0.616279  0.704545  0.216031  0.235612  0.258264\n",
              "1452  1.000000  1.000000  1.000000  0.517107  0.286587  0.519743\n",
              "\n",
              "[400 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fa720958-d68c-44b2-8a48-902446dafa3d\" class=\"colab-df-container\">\n",
              "    <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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.645161</td>\n",
              "      <td>0.697674</td>\n",
              "      <td>0.681818</td>\n",
              "      <td>0.412186</td>\n",
              "      <td>0.312427</td>\n",
              "      <td>0.369605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.830645</td>\n",
              "      <td>0.744186</td>\n",
              "      <td>0.795455</td>\n",
              "      <td>0.582111</td>\n",
              "      <td>0.260747</td>\n",
              "      <td>0.345500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.419355</td>\n",
              "      <td>0.383721</td>\n",
              "      <td>0.659091</td>\n",
              "      <td>0.253177</td>\n",
              "      <td>0.208715</td>\n",
              "      <td>0.343434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.604839</td>\n",
              "      <td>0.604651</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.389378</td>\n",
              "      <td>0.350951</td>\n",
              "      <td>0.377410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.419355</td>\n",
              "      <td>0.383721</td>\n",
              "      <td>0.659091</td>\n",
              "      <td>0.194526</td>\n",
              "      <td>0.213648</td>\n",
              "      <td>0.386364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1201</th>\n",
              "      <td>0.604839</td>\n",
              "      <td>0.604651</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.443548</td>\n",
              "      <td>0.266385</td>\n",
              "      <td>0.337236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1227</th>\n",
              "      <td>0.604839</td>\n",
              "      <td>0.604651</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.161290</td>\n",
              "      <td>0.325581</td>\n",
              "      <td>0.345271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1294</th>\n",
              "      <td>0.645161</td>\n",
              "      <td>0.697674</td>\n",
              "      <td>0.681818</td>\n",
              "      <td>0.330727</td>\n",
              "      <td>0.368804</td>\n",
              "      <td>0.383379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1319</th>\n",
              "      <td>0.709677</td>\n",
              "      <td>0.616279</td>\n",
              "      <td>0.704545</td>\n",
              "      <td>0.216031</td>\n",
              "      <td>0.235612</td>\n",
              "      <td>0.258264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1452</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.517107</td>\n",
              "      <td>0.286587</td>\n",
              "      <td>0.519743</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa720958-d68c-44b2-8a48-902446dafa3d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fa720958-d68c-44b2-8a48-902446dafa3d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fa720958-d68c-44b2-8a48-902446dafa3d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-59813434-dbec-4abe-b4d3-9c80e80d277f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e19e08f4-dfb3-43b3-9e6f-c0da7b3ab748\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border-radius: 50%;\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e19e08f4-dfb3-43b3-9e6f-c0da7b3ab748 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate on test set\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "print(y_pred.shape)\n",
        "print(type(y_pred))\n",
        "print(type(y_test))\n",
        "y_test = y_test.to_numpy()\n",
        "print(type(y_test))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwmS5tsmvasp",
        "outputId": "87fd6b2b-0af1-4707-bcc8-4902dd83a219"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13/13 [==============================] - 0s 1ms/step\n",
            "(400, 3)\n",
            "<class 'numpy.ndarray'>\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the number of rows in y_pred and y_test\n",
        "num_y_pred_rows = 400\n",
        "num_y_test_rows = 2000\n",
        "\n",
        "\n",
        "#Creating an empty list to store the values with closest coordinates\n",
        "closest_vals = []\n",
        "min_distances = []\n",
        "\n",
        "# Loop through each row in y_pred\n",
        "for i in range(num_y_pred_rows):\n",
        "  print(\"\\n Back to I loop\")\n",
        "\n",
        "\n",
        "  euc_dist_min = np.full([1 , 1], np.inf)\n",
        "  # Loop through each group of 5 rows in y_test\n",
        "  for j in range(i * 5, (i + 1) * 5):\n",
        "\n",
        "    print(\"\\nJ for loop starts\")\n",
        "    # euc_dist_min = float('inf')\n",
        "    print(\"j = \", j)\n",
        "\n",
        "    print(\"euc_dist_min = \", euc_dist_min)\n",
        "    print(\"euc_dist_min[0,0] = \", euc_dist_min[0,0])\n",
        "    # euc_dist_min = np.reshape(euc_dist_min, (-1,1))\n",
        "\n",
        "    print(\"euc_dist_min shape = \", euc_dist_min.shape)\n",
        "\n",
        "    # Calculate the Euclidean distance between y_pred[i] and the 5 rows in y_test\n",
        "\n",
        "    euc_dist = (((y_test[j,0]-y_pred[i,0])**2)+((y_test[j,1]-y_pred[i,1])**2)+((y_test[j,2]-y_pred[i,2])**2)**(0.5))\n",
        "    print(euc_dist)\n",
        "    print(\"euc_dist=\", euc_dist.shape)\n",
        "    euc_dist = np.reshape(euc_dist, (-1,1))\n",
        "    print(\"euc_dist after reshaping = \", euc_dist.shape)\n",
        "    print(type(euc_dist))\n",
        "\n",
        "    print(\"euc_dist type= \", type(euc_dist))\n",
        "    print(\"euc_dist_min type= \", type(euc_dist_min))\n",
        "\n",
        "    if (euc_dist[0,0]<euc_dist_min[0,0]):\n",
        "      print(\"This is getting executed\")\n",
        "      euc_dist_min[0,0] = euc_dist[0,0]\n",
        "      euc_dist_min[0,0] = euc_dist[0,0]\n",
        "      print(\"euc_dist_min = \", euc_dist_min)\n",
        "      index = j\n",
        "      print(\"index = \", index)\n",
        "\n",
        "    # Get the index of the closest row in y_test for this group\n",
        "    # closest_index = np.argmin(euc_dist)\n",
        "\n",
        "  min_distances.append(euc_dist_min[0,0])\n",
        "\n",
        "  # Add the euclidean distance of the closest row to the sum\n",
        "  print(\"y_test[index] = \", y_test[index])\n",
        "  print(\"y_test[index] type = \", type(y_test[index]))\n",
        "\n",
        "\n",
        "  closest_vals.append(y_test[index])\n",
        "\n",
        "\n",
        "# Convert the list to a NumPy ndarray\n",
        "min_distances_array = np.array(min_distances)\n",
        "print(\"min_distances_array = \", min_distances_array)\n",
        "\n",
        "\n",
        "print(closest_vals)\n",
        "print(len(closest_vals))\n",
        "\n",
        "# Convert the list to a NumPy ndarray\n",
        "closest_vals_array = np.array(closest_vals)\n",
        "print(\"closest_vals_array = \", closest_vals_array)\n",
        "print(\"closest_vals_array shape\", closest_vals_array.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yfv9Syy4vjvp",
        "outputId": "0a26f9f6-b517-4807-aa4a-6ed8ca129864"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.24799177 0.4442738  0.35864961]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1655\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07885533745773626\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.07885534]]\n",
            "index =  1655\n",
            "\n",
            "J for loop starts\n",
            "j =  1656\n",
            "euc_dist_min =  [[0.07885534]]\n",
            "euc_dist_min[0,0] =  0.07885533745773626\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0736010688682388\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.07360107]]\n",
            "index =  1656\n",
            "\n",
            "J for loop starts\n",
            "j =  1657\n",
            "euc_dist_min =  [[0.07360107]]\n",
            "euc_dist_min[0,0] =  0.0736010688682388\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06539640237264768\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0653964]]\n",
            "index =  1657\n",
            "\n",
            "J for loop starts\n",
            "j =  1658\n",
            "euc_dist_min =  [[0.0653964]]\n",
            "euc_dist_min[0,0] =  0.06539640237264768\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.27813331018805376\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1659\n",
            "euc_dist_min =  [[0.0653964]]\n",
            "euc_dist_min[0,0] =  0.06539640237264768\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04921548444506908\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.04921548]]\n",
            "index =  1659\n",
            "y_test[index] =  [0.23299177 0.24366543 0.30912317]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1660\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.18042220905845255\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.18042221]]\n",
            "index =  1660\n",
            "\n",
            "J for loop starts\n",
            "j =  1661\n",
            "euc_dist_min =  [[0.18042221]]\n",
            "euc_dist_min[0,0] =  0.18042220905845255\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06946309718383167\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0694631]]\n",
            "index =  1661\n",
            "\n",
            "J for loop starts\n",
            "j =  1662\n",
            "euc_dist_min =  [[0.0694631]]\n",
            "euc_dist_min[0,0] =  0.06946309718383167\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08298638202138998\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1663\n",
            "euc_dist_min =  [[0.0694631]]\n",
            "euc_dist_min[0,0] =  0.06946309718383167\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07041713364467792\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1664\n",
            "euc_dist_min =  [[0.0694631]]\n",
            "euc_dist_min[0,0] =  0.06946309718383167\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1532580329563507\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.17930944 0.26630175 0.35006718]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1665\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06717675615164612\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.06717676]]\n",
            "index =  1665\n",
            "\n",
            "J for loop starts\n",
            "j =  1666\n",
            "euc_dist_min =  [[0.06717676]]\n",
            "euc_dist_min[0,0] =  0.06717675615164612\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.12457816477029583\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1667\n",
            "euc_dist_min =  [[0.06717676]]\n",
            "euc_dist_min[0,0] =  0.06717675615164612\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.056154836990108974\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.05615484]]\n",
            "index =  1667\n",
            "\n",
            "J for loop starts\n",
            "j =  1668\n",
            "euc_dist_min =  [[0.05615484]]\n",
            "euc_dist_min[0,0] =  0.056154836990108974\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.13557525279771704\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1669\n",
            "euc_dist_min =  [[0.05615484]]\n",
            "euc_dist_min[0,0] =  0.056154836990108974\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.022389397258621484\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0223894]]\n",
            "index =  1669\n",
            "y_test[index] =  [0.33773089 0.37009317 0.36499906]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1670\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.2060784156416728\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.20607842]]\n",
            "index =  1670\n",
            "\n",
            "J for loop starts\n",
            "j =  1671\n",
            "euc_dist_min =  [[0.20607842]]\n",
            "euc_dist_min[0,0] =  0.2060784156416728\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.018641138799578204\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.01864114]]\n",
            "index =  1671\n",
            "\n",
            "J for loop starts\n",
            "j =  1672\n",
            "euc_dist_min =  [[0.01864114]]\n",
            "euc_dist_min[0,0] =  0.018641138799578204\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.13585661231569754\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1673\n",
            "euc_dist_min =  [[0.01864114]]\n",
            "euc_dist_min[0,0] =  0.018641138799578204\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.15195178189494346\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1674\n",
            "euc_dist_min =  [[0.01864114]]\n",
            "euc_dist_min[0,0] =  0.018641138799578204\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.023839714141471863\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.23734463 0.21924722 0.36963669]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1675\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06876780443843483\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0687678]]\n",
            "index =  1675\n",
            "\n",
            "J for loop starts\n",
            "j =  1676\n",
            "euc_dist_min =  [[0.0687678]]\n",
            "euc_dist_min[0,0] =  0.06876780443843483\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04508147611746953\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.04508148]]\n",
            "index =  1676\n",
            "\n",
            "J for loop starts\n",
            "j =  1677\n",
            "euc_dist_min =  [[0.04508148]]\n",
            "euc_dist_min[0,0] =  0.04508147611746953\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.2436481835386708\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1678\n",
            "euc_dist_min =  [[0.04508148]]\n",
            "euc_dist_min[0,0] =  0.04508147611746953\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.28349893794253134\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1679\n",
            "euc_dist_min =  [[0.04508148]]\n",
            "euc_dist_min[0,0] =  0.04508147611746953\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.046158042387007665\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.1920668  0.20468487 0.33009087]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1680\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.2777397551529999\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.27773976]]\n",
            "index =  1680\n",
            "\n",
            "J for loop starts\n",
            "j =  1681\n",
            "euc_dist_min =  [[0.27773976]]\n",
            "euc_dist_min[0,0] =  0.2777397551529999\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.03580282205136746\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03580282]]\n",
            "index =  1681\n",
            "\n",
            "J for loop starts\n",
            "j =  1682\n",
            "euc_dist_min =  [[0.03580282]]\n",
            "euc_dist_min[0,0] =  0.03580282205136746\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.17756507645358413\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1683\n",
            "euc_dist_min =  [[0.03580282]]\n",
            "euc_dist_min[0,0] =  0.03580282205136746\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.19338432657204535\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1684\n",
            "euc_dist_min =  [[0.03580282]]\n",
            "euc_dist_min[0,0] =  0.03580282205136746\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08190327407550436\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.3598286  0.24638025 0.41308051]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1685\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.11830872735500368\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.11830873]]\n",
            "index =  1685\n",
            "\n",
            "J for loop starts\n",
            "j =  1686\n",
            "euc_dist_min =  [[0.11830873]]\n",
            "euc_dist_min[0,0] =  0.11830872735500368\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.054087990856628264\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.05408799]]\n",
            "index =  1686\n",
            "\n",
            "J for loop starts\n",
            "j =  1687\n",
            "euc_dist_min =  [[0.05408799]]\n",
            "euc_dist_min[0,0] =  0.054087990856628264\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.004748279687895724\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.00474828]]\n",
            "index =  1687\n",
            "\n",
            "J for loop starts\n",
            "j =  1688\n",
            "euc_dist_min =  [[0.00474828]]\n",
            "euc_dist_min[0,0] =  0.004748279687895724\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.20525375677651558\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1689\n",
            "euc_dist_min =  [[0.00474828]]\n",
            "euc_dist_min[0,0] =  0.004748279687895724\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04675832890755775\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.33114727 0.34612272 0.36499183]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1690\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1168663666555353\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.11686637]]\n",
            "index =  1690\n",
            "\n",
            "J for loop starts\n",
            "j =  1691\n",
            "euc_dist_min =  [[0.11686637]]\n",
            "euc_dist_min[0,0] =  0.1168663666555353\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.03077299827297434\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.030773]]\n",
            "index =  1691\n",
            "\n",
            "J for loop starts\n",
            "j =  1692\n",
            "euc_dist_min =  [[0.030773]]\n",
            "euc_dist_min[0,0] =  0.03077299827297434\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.039555456967779484\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1693\n",
            "euc_dist_min =  [[0.030773]]\n",
            "euc_dist_min[0,0] =  0.03077299827297434\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.11592829251914025\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1694\n",
            "euc_dist_min =  [[0.030773]]\n",
            "euc_dist_min[0,0] =  0.03077299827297434\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.12350895231603834\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.46637021 0.31503762 0.36270644]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1695\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.13849664733250197\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.13849665]]\n",
            "index =  1695\n",
            "\n",
            "J for loop starts\n",
            "j =  1696\n",
            "euc_dist_min =  [[0.13849665]]\n",
            "euc_dist_min[0,0] =  0.13849664733250197\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.024149523257720077\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.02414952]]\n",
            "index =  1696\n",
            "\n",
            "J for loop starts\n",
            "j =  1697\n",
            "euc_dist_min =  [[0.02414952]]\n",
            "euc_dist_min[0,0] =  0.024149523257720077\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07920010636622828\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1698\n",
            "euc_dist_min =  [[0.02414952]]\n",
            "euc_dist_min[0,0] =  0.024149523257720077\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.5758267519476796\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1699\n",
            "euc_dist_min =  [[0.02414952]]\n",
            "euc_dist_min[0,0] =  0.024149523257720077\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0978117973910145\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.40387483 0.2107992  0.37669977]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1700\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.03962349466792602\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03962349]]\n",
            "index =  1700\n",
            "\n",
            "J for loop starts\n",
            "j =  1701\n",
            "euc_dist_min =  [[0.03962349]]\n",
            "euc_dist_min[0,0] =  0.03962349466792602\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.18824168560201637\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1702\n",
            "euc_dist_min =  [[0.03962349]]\n",
            "euc_dist_min[0,0] =  0.03962349466792602\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.41375395095681133\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1703\n",
            "euc_dist_min =  [[0.03962349]]\n",
            "euc_dist_min[0,0] =  0.03962349466792602\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06367438166566088\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1704\n",
            "euc_dist_min =  [[0.03962349]]\n",
            "euc_dist_min[0,0] =  0.03962349466792602\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.09723268136843419\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.24325003 0.28963707 0.39522785]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1705\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.03537483648694201\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03537484]]\n",
            "index =  1705\n",
            "\n",
            "J for loop starts\n",
            "j =  1706\n",
            "euc_dist_min =  [[0.03537484]]\n",
            "euc_dist_min[0,0] =  0.03537483648694201\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.24190764851616717\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1707\n",
            "euc_dist_min =  [[0.03537484]]\n",
            "euc_dist_min[0,0] =  0.03537483648694201\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.01210690002426681\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0121069]]\n",
            "index =  1707\n",
            "\n",
            "J for loop starts\n",
            "j =  1708\n",
            "euc_dist_min =  [[0.0121069]]\n",
            "euc_dist_min[0,0] =  0.01210690002426681\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06781628522680037\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1709\n",
            "euc_dist_min =  [[0.0121069]]\n",
            "euc_dist_min[0,0] =  0.01210690002426681\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.30033167472131816\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.3069391  0.26743872 0.36545819]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1710\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.21160083070610258\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.21160083]]\n",
            "index =  1710\n",
            "\n",
            "J for loop starts\n",
            "j =  1711\n",
            "euc_dist_min =  [[0.21160083]]\n",
            "euc_dist_min[0,0] =  0.21160083070610258\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06962633872790686\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.06962634]]\n",
            "index =  1711\n",
            "\n",
            "J for loop starts\n",
            "j =  1712\n",
            "euc_dist_min =  [[0.06962634]]\n",
            "euc_dist_min[0,0] =  0.06962633872790686\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.2004346031802427\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1713\n",
            "euc_dist_min =  [[0.06962634]]\n",
            "euc_dist_min[0,0] =  0.06962633872790686\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.3666233291261353\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1714\n",
            "euc_dist_min =  [[0.06962634]]\n",
            "euc_dist_min[0,0] =  0.06962633872790686\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06662785507931804\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.06662786]]\n",
            "index =  1714\n",
            "y_test[index] =  [0.15040048 0.34011869 0.30411937]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1715\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.16659958259463545\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.16659958]]\n",
            "index =  1715\n",
            "\n",
            "J for loop starts\n",
            "j =  1716\n",
            "euc_dist_min =  [[0.16659958]]\n",
            "euc_dist_min[0,0] =  0.16659958259463545\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04969697329954762\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.04969697]]\n",
            "index =  1716\n",
            "\n",
            "J for loop starts\n",
            "j =  1717\n",
            "euc_dist_min =  [[0.04969697]]\n",
            "euc_dist_min[0,0] =  0.04969697329954762\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1261585966890327\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1718\n",
            "euc_dist_min =  [[0.04969697]]\n",
            "euc_dist_min[0,0] =  0.04969697329954762\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.15795396404868098\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1719\n",
            "euc_dist_min =  [[0.04969697]]\n",
            "euc_dist_min[0,0] =  0.04969697329954762\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0312959183418607\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03129592]]\n",
            "index =  1719\n",
            "y_test[index] =  [0.22810418 0.18611316 0.37822326]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1720\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07113924118923334\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.07113924]]\n",
            "index =  1720\n",
            "\n",
            "J for loop starts\n",
            "j =  1721\n",
            "euc_dist_min =  [[0.07113924]]\n",
            "euc_dist_min[0,0] =  0.07113924118923334\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.043563369681486566\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.04356337]]\n",
            "index =  1721\n",
            "\n",
            "J for loop starts\n",
            "j =  1722\n",
            "euc_dist_min =  [[0.04356337]]\n",
            "euc_dist_min[0,0] =  0.043563369681486566\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.10619104042750124\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1723\n",
            "euc_dist_min =  [[0.04356337]]\n",
            "euc_dist_min[0,0] =  0.043563369681486566\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.14475536777057083\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1724\n",
            "euc_dist_min =  [[0.04356337]]\n",
            "euc_dist_min[0,0] =  0.043563369681486566\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.056525737050876525\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.2831955  0.47945717 0.35147055]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1725\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.006042732573033194\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.00604273]]\n",
            "index =  1725\n",
            "\n",
            "J for loop starts\n",
            "j =  1726\n",
            "euc_dist_min =  [[0.00604273]]\n",
            "euc_dist_min[0,0] =  0.006042732573033194\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.2199830793161343\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1727\n",
            "euc_dist_min =  [[0.00604273]]\n",
            "euc_dist_min[0,0] =  0.006042732573033194\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.12977068037952966\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1728\n",
            "euc_dist_min =  [[0.00604273]]\n",
            "euc_dist_min[0,0] =  0.006042732573033194\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.038113782399707405\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1729\n",
            "euc_dist_min =  [[0.00604273]]\n",
            "euc_dist_min[0,0] =  0.006042732573033194\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.059210967096688785\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.28996946 0.28752643 0.36070307]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1730\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.05356012427451455\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.05356012]]\n",
            "index =  1730\n",
            "\n",
            "J for loop starts\n",
            "j =  1731\n",
            "euc_dist_min =  [[0.05356012]]\n",
            "euc_dist_min[0,0] =  0.05356012427451455\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.19628348061341008\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1732\n",
            "euc_dist_min =  [[0.05356012]]\n",
            "euc_dist_min[0,0] =  0.05356012427451455\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.083309343663523\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1733\n",
            "euc_dist_min =  [[0.05356012]]\n",
            "euc_dist_min[0,0] =  0.05356012427451455\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.4011226834743507\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1734\n",
            "euc_dist_min =  [[0.05356012]]\n",
            "euc_dist_min[0,0] =  0.05356012427451455\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.24792887654508824\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.30555674 0.2561537  0.42807183]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1735\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.21783692709670582\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.21783693]]\n",
            "index =  1735\n",
            "\n",
            "J for loop starts\n",
            "j =  1736\n",
            "euc_dist_min =  [[0.21783693]]\n",
            "euc_dist_min[0,0] =  0.21783692709670582\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06992321925722828\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.06992322]]\n",
            "index =  1736\n",
            "\n",
            "J for loop starts\n",
            "j =  1737\n",
            "euc_dist_min =  [[0.06992322]]\n",
            "euc_dist_min[0,0] =  0.06992321925722828\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.008489377688453224\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.00848938]]\n",
            "index =  1737\n",
            "\n",
            "J for loop starts\n",
            "j =  1738\n",
            "euc_dist_min =  [[0.00848938]]\n",
            "euc_dist_min[0,0] =  0.008489377688453224\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.12395073354546533\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1739\n",
            "euc_dist_min =  [[0.00848938]]\n",
            "euc_dist_min[0,0] =  0.008489377688453224\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.05076688273740993\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.2848247  0.28636344 0.35491407]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1740\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08029076352397528\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.08029076]]\n",
            "index =  1740\n",
            "\n",
            "J for loop starts\n",
            "j =  1741\n",
            "euc_dist_min =  [[0.08029076]]\n",
            "euc_dist_min[0,0] =  0.08029076352397528\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.2374273702876489\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1742\n",
            "euc_dist_min =  [[0.08029076]]\n",
            "euc_dist_min[0,0] =  0.08029076352397528\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08618543652862087\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1743\n",
            "euc_dist_min =  [[0.08029076]]\n",
            "euc_dist_min[0,0] =  0.08029076352397528\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08302088056713441\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1744\n",
            "euc_dist_min =  [[0.08029076]]\n",
            "euc_dist_min[0,0] =  0.08029076352397528\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.27223924854185133\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.33594718 0.38285341 0.33490481]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1745\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.12731864509825264\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.12731865]]\n",
            "index =  1745\n",
            "\n",
            "J for loop starts\n",
            "j =  1746\n",
            "euc_dist_min =  [[0.12731865]]\n",
            "euc_dist_min[0,0] =  0.12731864509825264\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0026649925486745604\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.00266499]]\n",
            "index =  1746\n",
            "\n",
            "J for loop starts\n",
            "j =  1747\n",
            "euc_dist_min =  [[0.00266499]]\n",
            "euc_dist_min[0,0] =  0.0026649925486745604\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.025272663818343578\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1748\n",
            "euc_dist_min =  [[0.00266499]]\n",
            "euc_dist_min[0,0] =  0.0026649925486745604\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.008134035569190722\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1749\n",
            "euc_dist_min =  [[0.00266499]]\n",
            "euc_dist_min[0,0] =  0.0026649925486745604\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06646718401501508\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.27150569 0.31604809 0.36326348]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1750\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.2038935793106034\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.20389358]]\n",
            "index =  1750\n",
            "\n",
            "J for loop starts\n",
            "j =  1751\n",
            "euc_dist_min =  [[0.20389358]]\n",
            "euc_dist_min[0,0] =  0.2038935793106034\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06623331052906466\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.06623331]]\n",
            "index =  1751\n",
            "\n",
            "J for loop starts\n",
            "j =  1752\n",
            "euc_dist_min =  [[0.06623331]]\n",
            "euc_dist_min[0,0] =  0.06623331052906466\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04757846868160308\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.04757847]]\n",
            "index =  1752\n",
            "\n",
            "J for loop starts\n",
            "j =  1753\n",
            "euc_dist_min =  [[0.04757847]]\n",
            "euc_dist_min[0,0] =  0.04757846868160308\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.16458723072117942\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1754\n",
            "euc_dist_min =  [[0.04757847]]\n",
            "euc_dist_min[0,0] =  0.04757846868160308\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04556582448675079\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.04556582]]\n",
            "index =  1754\n",
            "y_test[index] =  [0.17181878 0.1701912  0.35880512]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1755\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07549259162495148\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.07549259]]\n",
            "index =  1755\n",
            "\n",
            "J for loop starts\n",
            "j =  1756\n",
            "euc_dist_min =  [[0.07549259]]\n",
            "euc_dist_min[0,0] =  0.07549259162495148\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06947180659734518\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.06947181]]\n",
            "index =  1756\n",
            "\n",
            "J for loop starts\n",
            "j =  1757\n",
            "euc_dist_min =  [[0.06947181]]\n",
            "euc_dist_min[0,0] =  0.06947180659734518\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.20013955871627886\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1758\n",
            "euc_dist_min =  [[0.06947181]]\n",
            "euc_dist_min[0,0] =  0.06947180659734518\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.018262760346027383\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.01826276]]\n",
            "index =  1758\n",
            "\n",
            "J for loop starts\n",
            "j =  1759\n",
            "euc_dist_min =  [[0.01826276]]\n",
            "euc_dist_min[0,0] =  0.018262760346027383\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0347364166918019\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.37459296 0.31552757 0.35177802]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1760\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.16333930304304717\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.1633393]]\n",
            "index =  1760\n",
            "\n",
            "J for loop starts\n",
            "j =  1761\n",
            "euc_dist_min =  [[0.1633393]]\n",
            "euc_dist_min[0,0] =  0.16333930304304717\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.030820488269873332\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03082049]]\n",
            "index =  1761\n",
            "\n",
            "J for loop starts\n",
            "j =  1762\n",
            "euc_dist_min =  [[0.03082049]]\n",
            "euc_dist_min[0,0] =  0.030820488269873332\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.024981149293811143\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.02498115]]\n",
            "index =  1762\n",
            "\n",
            "J for loop starts\n",
            "j =  1763\n",
            "euc_dist_min =  [[0.02498115]]\n",
            "euc_dist_min[0,0] =  0.024981149293811143\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07698629553684924\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1764\n",
            "euc_dist_min =  [[0.02498115]]\n",
            "euc_dist_min[0,0] =  0.024981149293811143\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.13919843835257442\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.227506   0.19372036 0.361658  ]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1765\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.24113786461826034\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.24113786]]\n",
            "index =  1765\n",
            "\n",
            "J for loop starts\n",
            "j =  1766\n",
            "euc_dist_min =  [[0.24113786]]\n",
            "euc_dist_min[0,0] =  0.24113786461826034\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.10228103509125416\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.10228104]]\n",
            "index =  1766\n",
            "\n",
            "J for loop starts\n",
            "j =  1767\n",
            "euc_dist_min =  [[0.10228104]]\n",
            "euc_dist_min[0,0] =  0.10228103509125416\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.23736656179014665\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1768\n",
            "euc_dist_min =  [[0.10228104]]\n",
            "euc_dist_min[0,0] =  0.10228103509125416\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.03253948946426704\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03253949]]\n",
            "index =  1768\n",
            "\n",
            "J for loop starts\n",
            "j =  1769\n",
            "euc_dist_min =  [[0.03253949]]\n",
            "euc_dist_min[0,0] =  0.03253948946426704\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.09850553160008337\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.40817827 0.26835471 0.36129252]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1770\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.3110061277074647\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.31100613]]\n",
            "index =  1770\n",
            "\n",
            "J for loop starts\n",
            "j =  1771\n",
            "euc_dist_min =  [[0.31100613]]\n",
            "euc_dist_min[0,0] =  0.3110061277074647\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06387961578550735\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.06387962]]\n",
            "index =  1771\n",
            "\n",
            "J for loop starts\n",
            "j =  1772\n",
            "euc_dist_min =  [[0.06387962]]\n",
            "euc_dist_min[0,0] =  0.06387961578550735\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04919781474479637\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.04919781]]\n",
            "index =  1772\n",
            "\n",
            "J for loop starts\n",
            "j =  1773\n",
            "euc_dist_min =  [[0.04919781]]\n",
            "euc_dist_min[0,0] =  0.04919781474479637\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06231972754297156\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1774\n",
            "euc_dist_min =  [[0.04919781]]\n",
            "euc_dist_min[0,0] =  0.04919781474479637\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0333145559753026\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03331456]]\n",
            "index =  1774\n",
            "y_test[index] =  [0.23724304 0.34618603 0.39758382]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1775\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.22338315259305558\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.22338315]]\n",
            "index =  1775\n",
            "\n",
            "J for loop starts\n",
            "j =  1776\n",
            "euc_dist_min =  [[0.22338315]]\n",
            "euc_dist_min[0,0] =  0.22338315259305558\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.09452036572224003\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.09452037]]\n",
            "index =  1776\n",
            "\n",
            "J for loop starts\n",
            "j =  1777\n",
            "euc_dist_min =  [[0.09452037]]\n",
            "euc_dist_min[0,0] =  0.09452036572224003\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.17394944521978326\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1778\n",
            "euc_dist_min =  [[0.09452037]]\n",
            "euc_dist_min[0,0] =  0.09452036572224003\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07864312567197121\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.07864313]]\n",
            "index =  1778\n",
            "\n",
            "J for loop starts\n",
            "j =  1779\n",
            "euc_dist_min =  [[0.07864313]]\n",
            "euc_dist_min[0,0] =  0.07864312567197121\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.03615023528939998\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03615024]]\n",
            "index =  1779\n",
            "y_test[index] =  [0.29488306 0.28881528 0.40402569]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1780\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04316221219383462\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.04316221]]\n",
            "index =  1780\n",
            "\n",
            "J for loop starts\n",
            "j =  1781\n",
            "euc_dist_min =  [[0.04316221]]\n",
            "euc_dist_min[0,0] =  0.04316221219383462\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.12818245741583453\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1782\n",
            "euc_dist_min =  [[0.04316221]]\n",
            "euc_dist_min[0,0] =  0.04316221219383462\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04252652211732021\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.04252652]]\n",
            "index =  1782\n",
            "\n",
            "J for loop starts\n",
            "j =  1783\n",
            "euc_dist_min =  [[0.04252652]]\n",
            "euc_dist_min[0,0] =  0.04252652211732021\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.09244200261693475\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1784\n",
            "euc_dist_min =  [[0.04252652]]\n",
            "euc_dist_min[0,0] =  0.04252652211732021\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04506963175052652\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.26492323 0.49344463 0.37086023]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1785\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.009383348369356297\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.00938335]]\n",
            "index =  1785\n",
            "\n",
            "J for loop starts\n",
            "j =  1786\n",
            "euc_dist_min =  [[0.00938335]]\n",
            "euc_dist_min[0,0] =  0.009383348369356297\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.09135982572514377\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1787\n",
            "euc_dist_min =  [[0.00938335]]\n",
            "euc_dist_min[0,0] =  0.009383348369356297\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04048324994287569\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1788\n",
            "euc_dist_min =  [[0.00938335]]\n",
            "euc_dist_min[0,0] =  0.009383348369356297\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.051431916876706604\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1789\n",
            "euc_dist_min =  [[0.00938335]]\n",
            "euc_dist_min[0,0] =  0.009383348369356297\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.10873679084048385\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.27085225 0.3487291  0.39350177]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1790\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1312822564915747\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.13128226]]\n",
            "index =  1790\n",
            "\n",
            "J for loop starts\n",
            "j =  1791\n",
            "euc_dist_min =  [[0.13128226]]\n",
            "euc_dist_min[0,0] =  0.1312822564915747\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.037147926421650726\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03714793]]\n",
            "index =  1791\n",
            "\n",
            "J for loop starts\n",
            "j =  1792\n",
            "euc_dist_min =  [[0.03714793]]\n",
            "euc_dist_min[0,0] =  0.037147926421650726\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.3196865885877573\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1793\n",
            "euc_dist_min =  [[0.03714793]]\n",
            "euc_dist_min[0,0] =  0.037147926421650726\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1412935634143825\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1794\n",
            "euc_dist_min =  [[0.03714793]]\n",
            "euc_dist_min[0,0] =  0.037147926421650726\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07976504219726255\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.22510848 0.34293758 0.32320408]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1795\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1089617232059632\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.10896172]]\n",
            "index =  1795\n",
            "\n",
            "J for loop starts\n",
            "j =  1796\n",
            "euc_dist_min =  [[0.10896172]]\n",
            "euc_dist_min[0,0] =  0.1089617232059632\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.024481483250996604\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.02448148]]\n",
            "index =  1796\n",
            "\n",
            "J for loop starts\n",
            "j =  1797\n",
            "euc_dist_min =  [[0.02448148]]\n",
            "euc_dist_min[0,0] =  0.024481483250996604\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.045836327961891465\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1798\n",
            "euc_dist_min =  [[0.02448148]]\n",
            "euc_dist_min[0,0] =  0.024481483250996604\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07117340867740254\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1799\n",
            "euc_dist_min =  [[0.02448148]]\n",
            "euc_dist_min[0,0] =  0.024481483250996604\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.003578312177624993\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.00357831]]\n",
            "index =  1799\n",
            "y_test[index] =  [0.30858642 0.26257546 0.36605613]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1800\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.21131381238749547\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.21131381]]\n",
            "index =  1800\n",
            "\n",
            "J for loop starts\n",
            "j =  1801\n",
            "euc_dist_min =  [[0.21131381]]\n",
            "euc_dist_min[0,0] =  0.21131381238749547\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.032359095548919886\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0323591]]\n",
            "index =  1801\n",
            "\n",
            "J for loop starts\n",
            "j =  1802\n",
            "euc_dist_min =  [[0.0323591]]\n",
            "euc_dist_min[0,0] =  0.032359095548919886\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.14343418628700388\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1803\n",
            "euc_dist_min =  [[0.0323591]]\n",
            "euc_dist_min[0,0] =  0.032359095548919886\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.11826711502387205\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1804\n",
            "euc_dist_min =  [[0.0323591]]\n",
            "euc_dist_min[0,0] =  0.032359095548919886\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.22024287859637762\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.37161815 0.36974081 0.34319006]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1805\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0588817833042512\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.05888178]]\n",
            "index =  1805\n",
            "\n",
            "J for loop starts\n",
            "j =  1806\n",
            "euc_dist_min =  [[0.05888178]]\n",
            "euc_dist_min[0,0] =  0.0588817833042512\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.22725104322413728\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1807\n",
            "euc_dist_min =  [[0.05888178]]\n",
            "euc_dist_min[0,0] =  0.0588817833042512\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04672936890004649\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.04672937]]\n",
            "index =  1807\n",
            "\n",
            "J for loop starts\n",
            "j =  1808\n",
            "euc_dist_min =  [[0.04672937]]\n",
            "euc_dist_min[0,0] =  0.04672936890004649\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.02555490429385726\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0255549]]\n",
            "index =  1808\n",
            "\n",
            "J for loop starts\n",
            "j =  1809\n",
            "euc_dist_min =  [[0.0255549]]\n",
            "euc_dist_min[0,0] =  0.02555490429385726\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.03783154705238641\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.35402753 0.32968261 0.39024631]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1810\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.12217237805604177\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.12217238]]\n",
            "index =  1810\n",
            "\n",
            "J for loop starts\n",
            "j =  1811\n",
            "euc_dist_min =  [[0.12217238]]\n",
            "euc_dist_min[0,0] =  0.12217237805604177\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.15697518666101257\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1812\n",
            "euc_dist_min =  [[0.12217238]]\n",
            "euc_dist_min[0,0] =  0.12217237805604177\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.14905223185782734\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1813\n",
            "euc_dist_min =  [[0.12217238]]\n",
            "euc_dist_min[0,0] =  0.12217237805604177\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0950897397934451\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.09508974]]\n",
            "index =  1813\n",
            "\n",
            "J for loop starts\n",
            "j =  1814\n",
            "euc_dist_min =  [[0.09508974]]\n",
            "euc_dist_min[0,0] =  0.0950897397934451\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0822941856923736\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.08229419]]\n",
            "index =  1814\n",
            "y_test[index] =  [0.19418713 0.1776602  0.42227402]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1815\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.11448162071038373\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.11448162]]\n",
            "index =  1815\n",
            "\n",
            "J for loop starts\n",
            "j =  1816\n",
            "euc_dist_min =  [[0.11448162]]\n",
            "euc_dist_min[0,0] =  0.11448162071038373\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.13953197771757747\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1817\n",
            "euc_dist_min =  [[0.11448162]]\n",
            "euc_dist_min[0,0] =  0.11448162071038373\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0678849896778635\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.06788499]]\n",
            "index =  1817\n",
            "\n",
            "J for loop starts\n",
            "j =  1818\n",
            "euc_dist_min =  [[0.06788499]]\n",
            "euc_dist_min[0,0] =  0.0678849896778635\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1638899731162981\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1819\n",
            "euc_dist_min =  [[0.06788499]]\n",
            "euc_dist_min[0,0] =  0.0678849896778635\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0031376480757527286\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.00313765]]\n",
            "index =  1819\n",
            "y_test[index] =  [0.22266827 0.30140507 0.35442973]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1820\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.042527169808011744\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.04252717]]\n",
            "index =  1820\n",
            "\n",
            "J for loop starts\n",
            "j =  1821\n",
            "euc_dist_min =  [[0.04252717]]\n",
            "euc_dist_min[0,0] =  0.042527169808011744\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.05983344431405548\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1822\n",
            "euc_dist_min =  [[0.04252717]]\n",
            "euc_dist_min[0,0] =  0.042527169808011744\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.13045523476101545\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1823\n",
            "euc_dist_min =  [[0.04252717]]\n",
            "euc_dist_min[0,0] =  0.042527169808011744\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.026329257137182954\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.02632926]]\n",
            "index =  1823\n",
            "\n",
            "J for loop starts\n",
            "j =  1824\n",
            "euc_dist_min =  [[0.02632926]]\n",
            "euc_dist_min[0,0] =  0.026329257137182954\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.10087886372856676\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.28253716 0.31371356 0.35672037]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1825\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06461340346275214\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0646134]]\n",
            "index =  1825\n",
            "\n",
            "J for loop starts\n",
            "j =  1826\n",
            "euc_dist_min =  [[0.0646134]]\n",
            "euc_dist_min[0,0] =  0.06461340346275214\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.039346459394837226\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03934646]]\n",
            "index =  1826\n",
            "\n",
            "J for loop starts\n",
            "j =  1827\n",
            "euc_dist_min =  [[0.03934646]]\n",
            "euc_dist_min[0,0] =  0.039346459394837226\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.25609254791388014\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1828\n",
            "euc_dist_min =  [[0.03934646]]\n",
            "euc_dist_min[0,0] =  0.039346459394837226\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.24647199801127725\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1829\n",
            "euc_dist_min =  [[0.03934646]]\n",
            "euc_dist_min[0,0] =  0.039346459394837226\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08436070059298346\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.2251295  0.27483157 0.35695248]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1830\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.014564869691768003\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.01456487]]\n",
            "index =  1830\n",
            "\n",
            "J for loop starts\n",
            "j =  1831\n",
            "euc_dist_min =  [[0.01456487]]\n",
            "euc_dist_min[0,0] =  0.014564869691768003\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0933969031126241\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1832\n",
            "euc_dist_min =  [[0.01456487]]\n",
            "euc_dist_min[0,0] =  0.014564869691768003\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.10077590982133272\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1833\n",
            "euc_dist_min =  [[0.01456487]]\n",
            "euc_dist_min[0,0] =  0.014564869691768003\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.014380779635509119\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.01438078]]\n",
            "index =  1833\n",
            "\n",
            "J for loop starts\n",
            "j =  1834\n",
            "euc_dist_min =  [[0.01438078]]\n",
            "euc_dist_min[0,0] =  0.014380779635509119\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07672987449387997\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.29996723 0.31988707 0.37335526]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1835\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.16789465507185444\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.16789466]]\n",
            "index =  1835\n",
            "\n",
            "J for loop starts\n",
            "j =  1836\n",
            "euc_dist_min =  [[0.16789466]]\n",
            "euc_dist_min[0,0] =  0.16789465507185444\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.3316579197001989\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1837\n",
            "euc_dist_min =  [[0.16789466]]\n",
            "euc_dist_min[0,0] =  0.16789465507185444\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.11654265433929972\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.11654265]]\n",
            "index =  1837\n",
            "\n",
            "J for loop starts\n",
            "j =  1838\n",
            "euc_dist_min =  [[0.11654265]]\n",
            "euc_dist_min[0,0] =  0.11654265433929972\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.013326047289420813\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.01332605]]\n",
            "index =  1838\n",
            "\n",
            "J for loop starts\n",
            "j =  1839\n",
            "euc_dist_min =  [[0.01332605]]\n",
            "euc_dist_min[0,0] =  0.013326047289420813\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.05779152526076841\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.36725531 0.28928321 0.35786821]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1840\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.23956554251482703\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.23956554]]\n",
            "index =  1840\n",
            "\n",
            "J for loop starts\n",
            "j =  1841\n",
            "euc_dist_min =  [[0.23956554]]\n",
            "euc_dist_min[0,0] =  0.23956554251482703\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0779558801250196\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.07795588]]\n",
            "index =  1841\n",
            "\n",
            "J for loop starts\n",
            "j =  1842\n",
            "euc_dist_min =  [[0.07795588]]\n",
            "euc_dist_min[0,0] =  0.0779558801250196\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.10598255178418847\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1843\n",
            "euc_dist_min =  [[0.07795588]]\n",
            "euc_dist_min[0,0] =  0.0779558801250196\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07806673683623343\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1844\n",
            "euc_dist_min =  [[0.07795588]]\n",
            "euc_dist_min[0,0] =  0.0779558801250196\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1153273814392213\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.24786546 0.28667747 0.28897847]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1845\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.014821382123142433\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.01482138]]\n",
            "index =  1845\n",
            "\n",
            "J for loop starts\n",
            "j =  1846\n",
            "euc_dist_min =  [[0.01482138]]\n",
            "euc_dist_min[0,0] =  0.014821382123142433\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.24664470876355588\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1847\n",
            "euc_dist_min =  [[0.01482138]]\n",
            "euc_dist_min[0,0] =  0.014821382123142433\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08630410741434485\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1848\n",
            "euc_dist_min =  [[0.01482138]]\n",
            "euc_dist_min[0,0] =  0.014821382123142433\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08048702062385013\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1849\n",
            "euc_dist_min =  [[0.01482138]]\n",
            "euc_dist_min[0,0] =  0.014821382123142433\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.4604298273383988\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.32043973 0.31417953 0.37194908]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1850\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.062406211411439996\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.06240621]]\n",
            "index =  1850\n",
            "\n",
            "J for loop starts\n",
            "j =  1851\n",
            "euc_dist_min =  [[0.06240621]]\n",
            "euc_dist_min[0,0] =  0.062406211411439996\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.2950342305571426\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1852\n",
            "euc_dist_min =  [[0.06240621]]\n",
            "euc_dist_min[0,0] =  0.062406211411439996\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.046508401229509756\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0465084]]\n",
            "index =  1852\n",
            "\n",
            "J for loop starts\n",
            "j =  1853\n",
            "euc_dist_min =  [[0.0465084]]\n",
            "euc_dist_min[0,0] =  0.046508401229509756\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1385298609247625\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1854\n",
            "euc_dist_min =  [[0.0465084]]\n",
            "euc_dist_min[0,0] =  0.046508401229509756\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.05799359473909136\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.18082388 0.3665133  0.32210587]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1855\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.027127559234064992\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.02712756]]\n",
            "index =  1855\n",
            "\n",
            "J for loop starts\n",
            "j =  1856\n",
            "euc_dist_min =  [[0.02712756]]\n",
            "euc_dist_min[0,0] =  0.027127559234064992\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06149070510782853\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1857\n",
            "euc_dist_min =  [[0.02712756]]\n",
            "euc_dist_min[0,0] =  0.027127559234064992\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04479297482187891\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1858\n",
            "euc_dist_min =  [[0.02712756]]\n",
            "euc_dist_min[0,0] =  0.027127559234064992\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.062397440164650944\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1859\n",
            "euc_dist_min =  [[0.02712756]]\n",
            "euc_dist_min[0,0] =  0.027127559234064992\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.2666526541214598\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.17689567 0.30713796 0.36183495]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1860\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.09416166311863167\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.09416166]]\n",
            "index =  1860\n",
            "\n",
            "J for loop starts\n",
            "j =  1861\n",
            "euc_dist_min =  [[0.09416166]]\n",
            "euc_dist_min[0,0] =  0.09416166311863167\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.21124706909292748\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1862\n",
            "euc_dist_min =  [[0.09416166]]\n",
            "euc_dist_min[0,0] =  0.09416166311863167\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1241504166607506\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1863\n",
            "euc_dist_min =  [[0.09416166]]\n",
            "euc_dist_min[0,0] =  0.09416166311863167\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.15667116027139638\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1864\n",
            "euc_dist_min =  [[0.09416166]]\n",
            "euc_dist_min[0,0] =  0.09416166311863167\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.16812164061377158\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.37244636 0.30146375 0.30195097]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1865\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08011357279166363\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.08011357]]\n",
            "index =  1865\n",
            "\n",
            "J for loop starts\n",
            "j =  1866\n",
            "euc_dist_min =  [[0.08011357]]\n",
            "euc_dist_min[0,0] =  0.08011357279166363\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06838844769956264\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.06838845]]\n",
            "index =  1866\n",
            "\n",
            "J for loop starts\n",
            "j =  1867\n",
            "euc_dist_min =  [[0.06838845]]\n",
            "euc_dist_min[0,0] =  0.06838844769956264\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07320817071192093\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1868\n",
            "euc_dist_min =  [[0.06838845]]\n",
            "euc_dist_min[0,0] =  0.06838844769956264\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.016919419645864648\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.01691942]]\n",
            "index =  1868\n",
            "\n",
            "J for loop starts\n",
            "j =  1869\n",
            "euc_dist_min =  [[0.01691942]]\n",
            "euc_dist_min[0,0] =  0.016919419645864648\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.3825053671601553\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.41759464 0.34594153 0.36589075]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1870\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0392454859447086\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03924549]]\n",
            "index =  1870\n",
            "\n",
            "J for loop starts\n",
            "j =  1871\n",
            "euc_dist_min =  [[0.03924549]]\n",
            "euc_dist_min[0,0] =  0.0392454859447086\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.2583884214556197\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1872\n",
            "euc_dist_min =  [[0.03924549]]\n",
            "euc_dist_min[0,0] =  0.0392454859447086\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.008936970889291093\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.00893697]]\n",
            "index =  1872\n",
            "\n",
            "J for loop starts\n",
            "j =  1873\n",
            "euc_dist_min =  [[0.00893697]]\n",
            "euc_dist_min[0,0] =  0.008936970889291093\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04533395637461246\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1874\n",
            "euc_dist_min =  [[0.00893697]]\n",
            "euc_dist_min[0,0] =  0.008936970889291093\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.15063585907144783\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.23029387 0.27373901 0.36383043]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1875\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07791676300141046\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.07791676]]\n",
            "index =  1875\n",
            "\n",
            "J for loop starts\n",
            "j =  1876\n",
            "euc_dist_min =  [[0.07791676]]\n",
            "euc_dist_min[0,0] =  0.07791676300141046\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.39497857216572974\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1877\n",
            "euc_dist_min =  [[0.07791676]]\n",
            "euc_dist_min[0,0] =  0.07791676300141046\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.034145898308315756\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0341459]]\n",
            "index =  1877\n",
            "\n",
            "J for loop starts\n",
            "j =  1878\n",
            "euc_dist_min =  [[0.0341459]]\n",
            "euc_dist_min[0,0] =  0.034145898308315756\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.3211806346067559\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1879\n",
            "euc_dist_min =  [[0.0341459]]\n",
            "euc_dist_min[0,0] =  0.034145898308315756\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0567223363266527\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.1916701  0.35735665 0.36366443]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1880\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.037106380438239536\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03710638]]\n",
            "index =  1880\n",
            "\n",
            "J for loop starts\n",
            "j =  1881\n",
            "euc_dist_min =  [[0.03710638]]\n",
            "euc_dist_min[0,0] =  0.037106380438239536\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.10092176610109428\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1882\n",
            "euc_dist_min =  [[0.03710638]]\n",
            "euc_dist_min[0,0] =  0.037106380438239536\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.11706806941309533\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1883\n",
            "euc_dist_min =  [[0.03710638]]\n",
            "euc_dist_min[0,0] =  0.037106380438239536\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.10105569686667527\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1884\n",
            "euc_dist_min =  [[0.03710638]]\n",
            "euc_dist_min[0,0] =  0.037106380438239536\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1555296481253071\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.24842499 0.24242424 0.3910061 ]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1885\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.11395894552045549\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.11395895]]\n",
            "index =  1885\n",
            "\n",
            "J for loop starts\n",
            "j =  1886\n",
            "euc_dist_min =  [[0.11395895]]\n",
            "euc_dist_min[0,0] =  0.11395894552045549\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.14910613960019997\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1887\n",
            "euc_dist_min =  [[0.11395895]]\n",
            "euc_dist_min[0,0] =  0.11395894552045549\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08644606577707067\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.08644607]]\n",
            "index =  1887\n",
            "\n",
            "J for loop starts\n",
            "j =  1888\n",
            "euc_dist_min =  [[0.08644607]]\n",
            "euc_dist_min[0,0] =  0.08644606577707067\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.12598410044373\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1889\n",
            "euc_dist_min =  [[0.08644607]]\n",
            "euc_dist_min[0,0] =  0.08644606577707067\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.11750615842532923\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.35916414 0.24950333 0.28068323]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1890\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.009459558716963168\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.00945956]]\n",
            "index =  1890\n",
            "\n",
            "J for loop starts\n",
            "j =  1891\n",
            "euc_dist_min =  [[0.00945956]]\n",
            "euc_dist_min[0,0] =  0.009459558716963168\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.02971564532953067\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1892\n",
            "euc_dist_min =  [[0.00945956]]\n",
            "euc_dist_min[0,0] =  0.009459558716963168\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.19053258049434954\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1893\n",
            "euc_dist_min =  [[0.00945956]]\n",
            "euc_dist_min[0,0] =  0.009459558716963168\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08491575500670301\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1894\n",
            "euc_dist_min =  [[0.00945956]]\n",
            "euc_dist_min[0,0] =  0.009459558716963168\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0073418430865079005\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.00734184]]\n",
            "index =  1894\n",
            "y_test[index] =  [0.22799904 0.3304417  0.35451503]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1895\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.051141707327469955\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.05114171]]\n",
            "index =  1895\n",
            "\n",
            "J for loop starts\n",
            "j =  1896\n",
            "euc_dist_min =  [[0.05114171]]\n",
            "euc_dist_min[0,0] =  0.051141707327469955\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.014515131383253648\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.01451513]]\n",
            "index =  1896\n",
            "\n",
            "J for loop starts\n",
            "j =  1897\n",
            "euc_dist_min =  [[0.01451513]]\n",
            "euc_dist_min[0,0] =  0.014515131383253648\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07522970408404475\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1898\n",
            "euc_dist_min =  [[0.01451513]]\n",
            "euc_dist_min[0,0] =  0.014515131383253648\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.5222877718245269\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1899\n",
            "euc_dist_min =  [[0.01451513]]\n",
            "euc_dist_min[0,0] =  0.014515131383253648\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.21581793008475247\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.22882165 0.39709803 0.37274585]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1900\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1299087036181473\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.1299087]]\n",
            "index =  1900\n",
            "\n",
            "J for loop starts\n",
            "j =  1901\n",
            "euc_dist_min =  [[0.1299087]]\n",
            "euc_dist_min[0,0] =  0.1299087036181473\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07448374003422646\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.07448374]]\n",
            "index =  1901\n",
            "\n",
            "J for loop starts\n",
            "j =  1902\n",
            "euc_dist_min =  [[0.07448374]]\n",
            "euc_dist_min[0,0] =  0.07448374003422646\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07066721756191958\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.07066722]]\n",
            "index =  1902\n",
            "\n",
            "J for loop starts\n",
            "j =  1903\n",
            "euc_dist_min =  [[0.07066722]]\n",
            "euc_dist_min[0,0] =  0.07066721756191958\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.009560712370751255\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.00956071]]\n",
            "index =  1903\n",
            "\n",
            "J for loop starts\n",
            "j =  1904\n",
            "euc_dist_min =  [[0.00956071]]\n",
            "euc_dist_min[0,0] =  0.009560712370751255\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.10438929326898558\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.25762889 0.28189419 0.34633901]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1905\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08613567611689227\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.08613568]]\n",
            "index =  1905\n",
            "\n",
            "J for loop starts\n",
            "j =  1906\n",
            "euc_dist_min =  [[0.08613568]]\n",
            "euc_dist_min[0,0] =  0.08613567611689227\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08640898482420308\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1907\n",
            "euc_dist_min =  [[0.08613568]]\n",
            "euc_dist_min[0,0] =  0.08613567611689227\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.03942510340952121\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0394251]]\n",
            "index =  1907\n",
            "\n",
            "J for loop starts\n",
            "j =  1908\n",
            "euc_dist_min =  [[0.0394251]]\n",
            "euc_dist_min[0,0] =  0.03942510340952121\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06540403597972906\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1909\n",
            "euc_dist_min =  [[0.0394251]]\n",
            "euc_dist_min[0,0] =  0.03942510340952121\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.12276729857704194\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.2674909  0.28165972 0.39522785]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1910\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.18405052920428858\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.18405053]]\n",
            "index =  1910\n",
            "\n",
            "J for loop starts\n",
            "j =  1911\n",
            "euc_dist_min =  [[0.18405053]]\n",
            "euc_dist_min[0,0] =  0.18405052920428858\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1024518192911025\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.10245182]]\n",
            "index =  1911\n",
            "\n",
            "J for loop starts\n",
            "j =  1912\n",
            "euc_dist_min =  [[0.10245182]]\n",
            "euc_dist_min[0,0] =  0.1024518192911025\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06310264259852907\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.06310264]]\n",
            "index =  1912\n",
            "\n",
            "J for loop starts\n",
            "j =  1913\n",
            "euc_dist_min =  [[0.06310264]]\n",
            "euc_dist_min[0,0] =  0.06310264259852907\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07280682889635921\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1914\n",
            "euc_dist_min =  [[0.06310264]]\n",
            "euc_dist_min[0,0] =  0.06310264259852907\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.026344204494071585\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0263442]]\n",
            "index =  1914\n",
            "y_test[index] =  [0.29960782 0.19480254 0.37772052]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1915\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.12351713119464947\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.12351713]]\n",
            "index =  1915\n",
            "\n",
            "J for loop starts\n",
            "j =  1916\n",
            "euc_dist_min =  [[0.12351713]]\n",
            "euc_dist_min[0,0] =  0.12351713119464947\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.19686641014711526\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1917\n",
            "euc_dist_min =  [[0.12351713]]\n",
            "euc_dist_min[0,0] =  0.12351713119464947\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.5463884312294968\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1918\n",
            "euc_dist_min =  [[0.12351713]]\n",
            "euc_dist_min[0,0] =  0.12351713119464947\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07088023856395649\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.07088024]]\n",
            "index =  1918\n",
            "\n",
            "J for loop starts\n",
            "j =  1919\n",
            "euc_dist_min =  [[0.07088024]]\n",
            "euc_dist_min[0,0] =  0.07088023856395649\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06006349337228127\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.06006349]]\n",
            "index =  1919\n",
            "y_test[index] =  [0.20250949 0.35915647 0.32164194]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1920\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.17472434288924094\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.17472434]]\n",
            "index =  1920\n",
            "\n",
            "J for loop starts\n",
            "j =  1921\n",
            "euc_dist_min =  [[0.17472434]]\n",
            "euc_dist_min[0,0] =  0.17472434288924094\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07001248554012983\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.07001249]]\n",
            "index =  1921\n",
            "\n",
            "J for loop starts\n",
            "j =  1922\n",
            "euc_dist_min =  [[0.07001249]]\n",
            "euc_dist_min[0,0] =  0.07001248554012983\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.059309288229961156\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.05930929]]\n",
            "index =  1922\n",
            "\n",
            "J for loop starts\n",
            "j =  1923\n",
            "euc_dist_min =  [[0.05930929]]\n",
            "euc_dist_min[0,0] =  0.059309288229961156\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.37574197550836336\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1924\n",
            "euc_dist_min =  [[0.05930929]]\n",
            "euc_dist_min[0,0] =  0.059309288229961156\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.42500279278575026\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.41848917 0.36880432 0.43889908]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1925\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08354562174277236\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.08354562]]\n",
            "index =  1925\n",
            "\n",
            "J for loop starts\n",
            "j =  1926\n",
            "euc_dist_min =  [[0.08354562]]\n",
            "euc_dist_min[0,0] =  0.08354562174277236\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.059100922774504634\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.05910092]]\n",
            "index =  1926\n",
            "\n",
            "J for loop starts\n",
            "j =  1927\n",
            "euc_dist_min =  [[0.05910092]]\n",
            "euc_dist_min[0,0] =  0.059100922774504634\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07282037707681667\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1928\n",
            "euc_dist_min =  [[0.05910092]]\n",
            "euc_dist_min[0,0] =  0.059100922774504634\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.19202670162884855\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1929\n",
            "euc_dist_min =  [[0.05910092]]\n",
            "euc_dist_min[0,0] =  0.059100922774504634\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.03803629206628677\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03803629]]\n",
            "index =  1929\n",
            "y_test[index] =  [0.31853785 0.30296715 0.34231037]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1930\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.37790143144887334\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.37790143]]\n",
            "index =  1930\n",
            "\n",
            "J for loop starts\n",
            "j =  1931\n",
            "euc_dist_min =  [[0.37790143]]\n",
            "euc_dist_min[0,0] =  0.37790143144887334\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.2539492684207522\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.25394927]]\n",
            "index =  1931\n",
            "\n",
            "J for loop starts\n",
            "j =  1932\n",
            "euc_dist_min =  [[0.25394927]]\n",
            "euc_dist_min[0,0] =  0.2539492684207522\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.11872201288355365\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.11872201]]\n",
            "index =  1932\n",
            "\n",
            "J for loop starts\n",
            "j =  1933\n",
            "euc_dist_min =  [[0.11872201]]\n",
            "euc_dist_min[0,0] =  0.11872201288355365\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.22684964663894244\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1934\n",
            "euc_dist_min =  [[0.11872201]]\n",
            "euc_dist_min[0,0] =  0.11872201288355365\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.16658114321584008\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.52361848 0.29569753 0.30671619]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1935\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.14117293832479694\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.14117294]]\n",
            "index =  1935\n",
            "\n",
            "J for loop starts\n",
            "j =  1936\n",
            "euc_dist_min =  [[0.14117294]]\n",
            "euc_dist_min[0,0] =  0.14117293832479694\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.4468858752334864\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1937\n",
            "euc_dist_min =  [[0.14117294]]\n",
            "euc_dist_min[0,0] =  0.14117293832479694\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07025715420815555\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.07025715]]\n",
            "index =  1937\n",
            "\n",
            "J for loop starts\n",
            "j =  1938\n",
            "euc_dist_min =  [[0.07025715]]\n",
            "euc_dist_min[0,0] =  0.07025715420815555\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.21300133982564973\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1939\n",
            "euc_dist_min =  [[0.07025715]]\n",
            "euc_dist_min[0,0] =  0.07025715420815555\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06543779487591692\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.06543779]]\n",
            "index =  1939\n",
            "y_test[index] =  [0.27967088 0.3505326  0.2950741 ]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1940\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.15952325032462528\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.15952325]]\n",
            "index =  1940\n",
            "\n",
            "J for loop starts\n",
            "j =  1941\n",
            "euc_dist_min =  [[0.15952325]]\n",
            "euc_dist_min[0,0] =  0.15952325032462528\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07140143859922066\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.07140144]]\n",
            "index =  1941\n",
            "\n",
            "J for loop starts\n",
            "j =  1942\n",
            "euc_dist_min =  [[0.07140144]]\n",
            "euc_dist_min[0,0] =  0.07140143859922066\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.060477727348223265\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.06047773]]\n",
            "index =  1942\n",
            "\n",
            "J for loop starts\n",
            "j =  1943\n",
            "euc_dist_min =  [[0.06047773]]\n",
            "euc_dist_min[0,0] =  0.060477727348223265\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.066254971258314\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1944\n",
            "euc_dist_min =  [[0.06047773]]\n",
            "euc_dist_min[0,0] =  0.060477727348223265\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.06232972082310087\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.3178536  0.25886775 0.42442397]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1945\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.24917029952421244\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.2491703]]\n",
            "index =  1945\n",
            "\n",
            "J for loop starts\n",
            "j =  1946\n",
            "euc_dist_min =  [[0.2491703]]\n",
            "euc_dist_min[0,0] =  0.24917029952421244\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.006404858412094538\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.00640486]]\n",
            "index =  1946\n",
            "\n",
            "J for loop starts\n",
            "j =  1947\n",
            "euc_dist_min =  [[0.00640486]]\n",
            "euc_dist_min[0,0] =  0.006404858412094538\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.012472666053148326\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1948\n",
            "euc_dist_min =  [[0.00640486]]\n",
            "euc_dist_min[0,0] =  0.006404858412094538\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1803954126843923\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1949\n",
            "euc_dist_min =  [[0.00640486]]\n",
            "euc_dist_min[0,0] =  0.006404858412094538\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0683406200606186\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.20874592 0.26464205 0.35927993]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1950\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1467292770604099\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.14672928]]\n",
            "index =  1950\n",
            "\n",
            "J for loop starts\n",
            "j =  1951\n",
            "euc_dist_min =  [[0.14672928]]\n",
            "euc_dist_min[0,0] =  0.1467292770604099\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.42583143124377976\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1952\n",
            "euc_dist_min =  [[0.14672928]]\n",
            "euc_dist_min[0,0] =  0.1467292770604099\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.15441939412693495\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1953\n",
            "euc_dist_min =  [[0.14672928]]\n",
            "euc_dist_min[0,0] =  0.1467292770604099\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.14737548146188084\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1954\n",
            "euc_dist_min =  [[0.14672928]]\n",
            "euc_dist_min[0,0] =  0.1467292770604099\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.13654011847565367\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.13654012]]\n",
            "index =  1954\n",
            "y_test[index] =  [0.30111918 0.21262912 0.48236367]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1955\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.09009762935800127\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.09009763]]\n",
            "index =  1955\n",
            "\n",
            "J for loop starts\n",
            "j =  1956\n",
            "euc_dist_min =  [[0.09009763]]\n",
            "euc_dist_min[0,0] =  0.09009762935800127\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.21302831059747984\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1957\n",
            "euc_dist_min =  [[0.09009763]]\n",
            "euc_dist_min[0,0] =  0.09009762935800127\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.13305455044470804\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1958\n",
            "euc_dist_min =  [[0.09009763]]\n",
            "euc_dist_min[0,0] =  0.09009762935800127\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.04888620567855169\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.04888621]]\n",
            "index =  1958\n",
            "\n",
            "J for loop starts\n",
            "j =  1959\n",
            "euc_dist_min =  [[0.04888621]]\n",
            "euc_dist_min[0,0] =  0.04888620567855169\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.05367545819639381\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.37243238 0.31208143 0.39941065]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1960\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.13200195161111755\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.13200195]]\n",
            "index =  1960\n",
            "\n",
            "J for loop starts\n",
            "j =  1961\n",
            "euc_dist_min =  [[0.13200195]]\n",
            "euc_dist_min[0,0] =  0.13200195161111755\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1138728613547201\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.11387286]]\n",
            "index =  1961\n",
            "\n",
            "J for loop starts\n",
            "j =  1962\n",
            "euc_dist_min =  [[0.11387286]]\n",
            "euc_dist_min[0,0] =  0.1138728613547201\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.156305924708535\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1963\n",
            "euc_dist_min =  [[0.11387286]]\n",
            "euc_dist_min[0,0] =  0.1138728613547201\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0974515174099576\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.09745152]]\n",
            "index =  1963\n",
            "\n",
            "J for loop starts\n",
            "j =  1964\n",
            "euc_dist_min =  [[0.09745152]]\n",
            "euc_dist_min[0,0] =  0.0974515174099576\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1309105448385039\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.36709354 0.43237843 0.31519016]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1965\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.2082311422117403\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.20823114]]\n",
            "index =  1965\n",
            "\n",
            "J for loop starts\n",
            "j =  1966\n",
            "euc_dist_min =  [[0.20823114]]\n",
            "euc_dist_min[0,0] =  0.2082311422117403\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.20433662040618017\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.20433662]]\n",
            "index =  1966\n",
            "\n",
            "J for loop starts\n",
            "j =  1967\n",
            "euc_dist_min =  [[0.20433662]]\n",
            "euc_dist_min[0,0] =  0.20433662040618017\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08572553666881944\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.08572554]]\n",
            "index =  1967\n",
            "\n",
            "J for loop starts\n",
            "j =  1968\n",
            "euc_dist_min =  [[0.08572554]]\n",
            "euc_dist_min[0,0] =  0.08572553666881944\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.039662184624309094\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03966218]]\n",
            "index =  1968\n",
            "\n",
            "J for loop starts\n",
            "j =  1969\n",
            "euc_dist_min =  [[0.03966218]]\n",
            "euc_dist_min[0,0] =  0.039662184624309094\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0353276037115537\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0353276]]\n",
            "index =  1969\n",
            "y_test[index] =  [0.3429803  0.25592639 0.41066894]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1970\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.03875576178041233\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03875576]]\n",
            "index =  1970\n",
            "\n",
            "J for loop starts\n",
            "j =  1971\n",
            "euc_dist_min =  [[0.03875576]]\n",
            "euc_dist_min[0,0] =  0.03875576178041233\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.07179616992504906\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1972\n",
            "euc_dist_min =  [[0.03875576]]\n",
            "euc_dist_min[0,0] =  0.03875576178041233\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.14894117563531692\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1973\n",
            "euc_dist_min =  [[0.03875576]]\n",
            "euc_dist_min[0,0] =  0.03875576178041233\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.10567287411306912\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1974\n",
            "euc_dist_min =  [[0.03875576]]\n",
            "euc_dist_min[0,0] =  0.03875576178041233\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.05061537561416264\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.12944003 0.36666813 0.37047208]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1975\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.238929131182382\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.23892913]]\n",
            "index =  1975\n",
            "\n",
            "J for loop starts\n",
            "j =  1976\n",
            "euc_dist_min =  [[0.23892913]]\n",
            "euc_dist_min[0,0] =  0.238929131182382\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.061605696030213326\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0616057]]\n",
            "index =  1976\n",
            "\n",
            "J for loop starts\n",
            "j =  1977\n",
            "euc_dist_min =  [[0.0616057]]\n",
            "euc_dist_min[0,0] =  0.061605696030213326\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.009834596350093951\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.0098346]]\n",
            "index =  1977\n",
            "\n",
            "J for loop starts\n",
            "j =  1978\n",
            "euc_dist_min =  [[0.0098346]]\n",
            "euc_dist_min[0,0] =  0.009834596350093951\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.10501480737740439\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1979\n",
            "euc_dist_min =  [[0.0098346]]\n",
            "euc_dist_min[0,0] =  0.009834596350093951\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.18228735693713627\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.28086031 0.32083614 0.37934473]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1980\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.12604605942888347\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.12604606]]\n",
            "index =  1980\n",
            "\n",
            "J for loop starts\n",
            "j =  1981\n",
            "euc_dist_min =  [[0.12604606]]\n",
            "euc_dist_min[0,0] =  0.12604605942888347\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1073468597922963\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.10734686]]\n",
            "index =  1981\n",
            "\n",
            "J for loop starts\n",
            "j =  1982\n",
            "euc_dist_min =  [[0.10734686]]\n",
            "euc_dist_min[0,0] =  0.1073468597922963\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1446914330897841\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1983\n",
            "euc_dist_min =  [[0.10734686]]\n",
            "euc_dist_min[0,0] =  0.1073468597922963\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.1365854926333524\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1984\n",
            "euc_dist_min =  [[0.10734686]]\n",
            "euc_dist_min[0,0] =  0.1073468597922963\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.09403376955692302\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.09403377]]\n",
            "index =  1984\n",
            "y_test[index] =  [0.19782568 0.18523589 0.27571297]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1985\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.16074802224003998\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.16074802]]\n",
            "index =  1985\n",
            "\n",
            "J for loop starts\n",
            "j =  1986\n",
            "euc_dist_min =  [[0.16074802]]\n",
            "euc_dist_min[0,0] =  0.16074802224003998\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.08792223649577259\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.08792224]]\n",
            "index =  1986\n",
            "\n",
            "J for loop starts\n",
            "j =  1987\n",
            "euc_dist_min =  [[0.08792224]]\n",
            "euc_dist_min[0,0] =  0.08792223649577259\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.11160244061497875\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1988\n",
            "euc_dist_min =  [[0.08792224]]\n",
            "euc_dist_min[0,0] =  0.08792223649577259\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.03649918275907024\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03649918]]\n",
            "index =  1988\n",
            "\n",
            "J for loop starts\n",
            "j =  1989\n",
            "euc_dist_min =  [[0.03649918]]\n",
            "euc_dist_min[0,0] =  0.03649918275907024\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.011733019551843506\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.01173302]]\n",
            "index =  1989\n",
            "y_test[index] =  [0.38403602 0.39405183 0.37164231]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1990\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.2679988783968622\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.26799888]]\n",
            "index =  1990\n",
            "\n",
            "J for loop starts\n",
            "j =  1991\n",
            "euc_dist_min =  [[0.26799888]]\n",
            "euc_dist_min[0,0] =  0.2679988783968622\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.030093218156823934\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.03009322]]\n",
            "index =  1991\n",
            "\n",
            "J for loop starts\n",
            "j =  1992\n",
            "euc_dist_min =  [[0.03009322]]\n",
            "euc_dist_min[0,0] =  0.030093218156823934\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0028547050415749283\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.00285471]]\n",
            "index =  1992\n",
            "\n",
            "J for loop starts\n",
            "j =  1993\n",
            "euc_dist_min =  [[0.00285471]]\n",
            "euc_dist_min[0,0] =  0.0028547050415749283\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.11314828699188574\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1994\n",
            "euc_dist_min =  [[0.00285471]]\n",
            "euc_dist_min[0,0] =  0.0028547050415749283\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0750400684829359\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.21645543 0.28148549 0.3568734 ]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "\n",
            " Back to I loop\n",
            "\n",
            "J for loop starts\n",
            "j =  1995\n",
            "euc_dist_min =  [[inf]]\n",
            "euc_dist_min[0,0] =  inf\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.0480397467655607\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "This is getting executed\n",
            "euc_dist_min =  [[0.04803975]]\n",
            "index =  1995\n",
            "\n",
            "J for loop starts\n",
            "j =  1996\n",
            "euc_dist_min =  [[0.04803975]]\n",
            "euc_dist_min[0,0] =  0.0480397467655607\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.23537427797325333\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1997\n",
            "euc_dist_min =  [[0.04803975]]\n",
            "euc_dist_min[0,0] =  0.0480397467655607\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.15955514823773878\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1998\n",
            "euc_dist_min =  [[0.04803975]]\n",
            "euc_dist_min[0,0] =  0.0480397467655607\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.26590998858422127\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "\n",
            "J for loop starts\n",
            "j =  1999\n",
            "euc_dist_min =  [[0.04803975]]\n",
            "euc_dist_min[0,0] =  0.0480397467655607\n",
            "euc_dist_min shape =  (1, 1)\n",
            "0.098705172295826\n",
            "euc_dist= ()\n",
            "euc_dist after reshaping =  (1, 1)\n",
            "<class 'numpy.ndarray'>\n",
            "euc_dist type=  <class 'numpy.ndarray'>\n",
            "euc_dist_min type=  <class 'numpy.ndarray'>\n",
            "y_test[index] =  [0.35056752 0.26112184 0.42904991]\n",
            "y_test[index] type =  <class 'numpy.ndarray'>\n",
            "min_distances_array =  [0.02348927 0.04498581 0.05167205 0.02529079 0.02538926 0.0394251\n",
            " 0.02512632 0.01194826 0.02189052 0.03701305 0.03942494 0.01569474\n",
            " 0.07981233 0.04967397 0.02426715 0.08384883 0.01152486 0.03176843\n",
            " 0.0281509  0.0239532  0.02033282 0.01653926 0.03119388 0.06264423\n",
            " 0.02406407 0.04018167 0.06491569 0.03854076 0.02166041 0.05474258\n",
            " 0.00321831 0.07831283 0.03492989 0.06360227 0.06097046 0.10414157\n",
            " 0.01249389 0.05887355 0.03671246 0.03254729 0.01782304 0.04815419\n",
            " 0.01936083 0.03558024 0.03658458 0.05675118 0.02572988 0.01075068\n",
            " 0.08706501 0.02512695 0.01297199 0.03091999 0.02777724 0.10707806\n",
            " 0.03334567 0.13209626 0.03323385 0.09469399 0.02957932 0.00617657\n",
            " 0.04022525 0.03425142 0.03714739 0.01560013 0.05923221 0.00399378\n",
            " 0.02572185 0.02027565 0.03102466 0.04660593 0.05551317 0.01343479\n",
            " 0.04394551 0.01415509 0.02745521 0.00913367 0.05917298 0.03654703\n",
            " 0.01802657 0.04013256 0.06591612 0.08278095 0.03017779 0.07102418\n",
            " 0.04467907 0.02212004 0.02639561 0.01900108 0.03925905 0.04549072\n",
            " 0.07744837 0.06129379 0.01821613 0.05651928 0.0571845  0.05608299\n",
            " 0.02429326 0.10453898 0.03167374 0.0180953  0.07422632 0.04243413\n",
            " 0.01894207 0.00237191 0.06922674 0.01675644 0.0305387  0.01820248\n",
            " 0.01470561 0.0350383  0.06676835 0.01724624 0.01928734 0.04571857\n",
            " 0.01013323 0.00111639 0.02109407 0.05373065 0.06450153 0.0342249\n",
            " 0.00497931 0.02629388 0.05035394 0.01385132 0.05350152 0.07705299\n",
            " 0.03634489 0.05588487 0.04082892 0.05108419 0.03267377 0.03901831\n",
            " 0.03458321 0.02788171 0.0710672  0.0136074  0.01325029 0.07531126\n",
            " 0.0149642  0.01738327 0.02541914 0.03630036 0.02428952 0.08438569\n",
            " 0.02357551 0.04732114 0.01982636 0.01544804 0.01580136 0.03342632\n",
            " 0.01281047 0.04939021 0.02687871 0.00413375 0.04871763 0.03449914\n",
            " 0.01257291 0.02258011 0.0515434  0.00260501 0.02248449 0.08045407\n",
            " 0.03764015 0.02130442 0.05062601 0.13054602 0.0312174  0.0816055\n",
            " 0.01641577 0.02821841 0.02002833 0.02807423 0.04662552 0.02648453\n",
            " 0.01891197 0.04702989 0.01272655 0.03151413 0.02275301 0.01199884\n",
            " 0.09516555 0.02913868 0.0176249  0.02406887 0.01440659 0.02285063\n",
            " 0.00319891 0.05259814 0.00802525 0.0415134  0.0147427  0.04176849\n",
            " 0.05630391 0.02313629 0.03613717 0.03083175 0.06185065 0.15054627\n",
            " 0.03406834 0.02327342 0.01958081 0.03669841 0.03226157 0.10344279\n",
            " 0.01436331 0.06601808 0.05445839 0.07231999 0.04621823 0.0109501\n",
            " 0.01516822 0.03538133 0.02948525 0.06562214 0.02049826 0.04949418\n",
            " 0.02753752 0.00338876 0.00974496 0.02366855 0.01131686 0.07086641\n",
            " 0.03937733 0.02300815 0.08566602 0.02826122 0.01923505 0.05585616\n",
            " 0.01123939 0.04099125 0.05645385 0.03784066 0.04471166 0.03351302\n",
            " 0.02073378 0.02499233 0.05087249 0.03157137 0.04946548 0.04460368\n",
            " 0.0578711  0.05221325 0.02511789 0.05244462 0.0655022  0.05294071\n",
            " 0.01351134 0.02360818 0.03792366 0.00319677 0.11287688 0.04762737\n",
            " 0.02302793 0.02212785 0.00182341 0.02989402 0.07354512 0.08609094\n",
            " 0.03133824 0.02376692 0.02287051 0.02884086 0.02706714 0.0427418\n",
            " 0.02755855 0.07117192 0.01379893 0.01816837 0.03077884 0.04557613\n",
            " 0.03277071 0.05663578 0.02088645 0.02244083 0.02264019 0.01280433\n",
            " 0.02742819 0.02815131 0.01323317 0.05909051 0.020637   0.02142949\n",
            " 0.04412965 0.019043   0.00202898 0.03900503 0.08046615 0.00457868\n",
            " 0.05774797 0.01133735 0.03761431 0.0179138  0.0579053  0.02717034\n",
            " 0.00930935 0.02909427 0.0482648  0.05299023 0.01623745 0.02883306\n",
            " 0.00256294 0.08822613 0.03406915 0.04766485 0.05879884 0.01927332\n",
            " 0.06382408 0.0375903  0.03004721 0.00619889 0.00968482 0.05434154\n",
            " 0.01206263 0.02203647 0.02372738 0.03591603 0.02076301 0.05531699\n",
            " 0.06162107 0.02203093 0.04587086 0.01193054 0.01288833 0.11265337\n",
            " 0.03808817 0.00734759 0.04341658 0.04776246 0.02200365 0.03399505\n",
            " 0.03337971 0.04921548 0.0694631  0.0223894  0.01864114 0.04508148\n",
            " 0.03580282 0.00474828 0.030773   0.02414952 0.03962349 0.0121069\n",
            " 0.06662786 0.03129592 0.04356337 0.00604273 0.05356012 0.00848938\n",
            " 0.08029076 0.00266499 0.04556582 0.01826276 0.02498115 0.03253949\n",
            " 0.03331456 0.03615024 0.04252652 0.00938335 0.03714793 0.00357831\n",
            " 0.0323591  0.0255549  0.08229419 0.00313765 0.02632926 0.03934646\n",
            " 0.01438078 0.01332605 0.07795588 0.01482138 0.0465084  0.02712756\n",
            " 0.09416166 0.01691942 0.00893697 0.0341459  0.03710638 0.08644607\n",
            " 0.00734184 0.01451513 0.00956071 0.0394251  0.0263442  0.06006349\n",
            " 0.05930929 0.03803629 0.11872201 0.06543779 0.06047773 0.00640486\n",
            " 0.13654012 0.04888621 0.09745152 0.0353276  0.03875576 0.0098346\n",
            " 0.09403377 0.01173302 0.00285471 0.04803975]\n",
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array([0.23540747, 0.38712918, 0.35700956]), array([0.20029635, 0.34171336, 0.36306493]), array([0.28368169, 0.31408683, 0.35233031]), array([0.26192808, 0.27723837, 0.40755972]), array([0.37847702, 0.56674151, 0.4019609 ]), array([0.18636699, 0.23192376, 0.26065786]), array([0.28052214, 0.37243552, 0.36567608]), array([0.18439962, 0.30901057, 0.38725551]), array([0.3359569 , 0.36887097, 0.33162087]), array([0.21440327, 0.3050144 , 0.28596899]), array([0.37544639, 0.27778498, 0.34064114]), array([0.2071751 , 0.20364407, 0.35437675]), array([0.29309935, 0.30157552, 0.37393144]), array([0.17659404, 0.43208583, 0.34883043]), array([0.1671449 , 0.29349903, 0.35567155]), array([0.18762476, 0.20857712, 0.30134645]), array([0.25237139, 0.32760831, 0.33720147]), array([0.45484848, 0.24712802, 0.35420343]), array([0.20539501, 0.2779431 , 0.38044155]), array([0.3523796 , 0.38438889, 0.39046736]), array([0.32184624, 0.24864614, 0.36086682]), array([0.28736476, 0.30607423, 0.35580464]), array([0.31194367, 0.30495534, 0.3640673 ]), array([0.17220466, 0.21164826, 0.32505415]), array([0.25484108, 0.26243922, 0.39470389]), array([0.25293287, 0.35057945, 0.34925981]), array([0.51972468, 0.48129246, 0.35942901]), array([0.35380344, 0.3122614 , 0.3998967 ]), array([0.32229619, 0.31972027, 0.34458349]), array([0.42737125, 0.46020814, 0.4009022 ]), array([0.2758577 , 0.25163349, 0.3754347 ]), array([0.61371901, 0.30689366, 0.44391205]), array([0.23704734, 0.31002187, 0.39234864]), array([0.31359901, 0.4372499 , 0.41252957]), array([0.2792128 , 0.41370456, 0.32823622]), array([0.4157718 , 0.26085184, 0.38881563]), array([0.35762074, 0.32451093, 0.36442251]), array([0.45520896, 0.41870698, 0.3764924 ]), array([0.38029908, 0.38746451, 0.32956187]), array([0.41046455, 0.29522641, 0.34721371]), array([0.2133515 , 0.4936185 , 0.38726157]), array([0.29909496, 0.31218182, 0.40125918]), array([0.27172448, 0.1909697 , 0.40470271]), array([0.40375595, 0.25864439, 0.42493244]), array([0.37711148, 0.36368215, 0.38059623]), array([0.35990162, 0.32169204, 0.31483573]), array([0.20601292, 0.38300359, 0.30272748]), array([0.20883986, 0.39921594, 0.33659555]), array([0.20911062, 0.27999974, 0.36683033]), array([0.17952678, 0.24287653, 0.3712822 ]), array([0.50190603, 0.34191286, 0.37450523]), array([0.31702752, 0.28260704, 0.35704107]), array([0.32175337, 0.33780367, 0.36046785]), array([0.21101549, 0.24699991, 0.36613267]), array([0.36642614, 0.26474135, 0.40196399]), array([0.17541712, 0.24857761, 0.35511413]), array([0.23920375, 0.24998481, 0.35573552]), array([0.22743684, 0.28246331, 0.33234181]), array([0.52499762, 0.33860122, 0.35567155]), array([0.18571532, 0.24343422, 0.40528596]), array([0.32074133, 0.37871621, 0.33697823]), array([0.18279601, 0.38652026, 0.34834153]), array([0.24122249, 0.23432086, 0.38176197]), array([0.18758863, 0.26196236, 0.3327619 ]), array([0.15481265, 0.32506979, 0.34132216]), array([0.20810771, 0.27486052, 0.31589069]), array([0.31506106, 0.33199843, 0.3470547 ]), array([0.34889721, 0.30814263, 0.46379714]), array([0.37534738, 0.302669  , 0.35581119]), array([0.37706429, 0.270566  , 0.35937228]), array([0.39275159, 0.27832326, 0.34292914]), array([0.26474541, 0.3181692 , 0.40832895]), array([0.28451598, 0.33109285, 0.38595275]), array([0.21879166, 0.21835929, 0.40634874]), array([0.3427814 , 0.25921697, 0.40173   ]), array([0.27766382, 0.39649299, 0.34607728]), array([0.29297981, 0.31948245, 0.38912681]), array([0.23049843, 0.28042501, 0.36782323]), array([0.26636132, 0.28739316, 0.32850596]), array([0.21668151, 0.28248527, 0.354016  ]), array([0.37265765, 0.28033995, 0.35627002]), array([0.44303436, 0.38371106, 0.33192952]), array([0.33386302, 0.3646911 , 0.37082978]), array([0.35065348, 0.31069632, 0.3788945 ]), array([0.25089461, 0.35295743, 0.31636678]), array([0.38515348, 0.29971126, 0.38951856]), array([0.2919265 , 0.33578999, 0.37692493]), array([0.26295242, 0.28492289, 0.31689066]), array([0.50596495, 0.29437199, 0.36839113]), array([0.37534496, 0.28591541, 0.37853562]), array([0.23040958, 0.49425632, 0.39026761]), array([0.21973123, 0.29180054, 0.34616097]), array([0.20499413, 0.30186732, 0.39026761]), array([0.22970848, 0.29646345, 0.3772569 ]), array([0.43503887, 0.51102814, 0.43279881]), array([0.38415129, 0.30591953, 0.3446799 ]), array([0.41592236, 0.31190482, 0.37774631]), array([0.27104701, 0.42189335, 0.36635546]), array([0.24532193, 0.25899307, 0.32958556]), array([0.24527407, 0.37479132, 0.35352064]), array([0.2125618 , 0.30304821, 0.37885973]), array([0.21685828, 0.23618582, 0.34422049]), array([0.28632979, 0.31841359, 0.37303395]), array([0.44071039, 0.36436813, 0.30669174]), array([0.45382541, 0.30846022, 0.36132902]), array([0.21440327, 0.29432612, 0.3272913 ]), array([0.29184083, 0.27462161, 0.42091298]), array([0.26245645, 0.31620513, 0.37420886]), array([0.53436308, 0.26669003, 0.4326046 ]), array([0.22037155, 0.33169107, 0.39866767]), array([0.25285228, 0.48871184, 0.3796329 ]), array([0.29152521, 0.33796444, 0.35345837]), array([0.18131512, 0.3241902 , 0.35519615]), array([0.25227405, 0.31810907, 0.4091146 ]), array([0.33986657, 0.38153555, 0.36029742]), array([0.32003244, 0.26132541, 0.37378563]), array([0.35403571, 0.29939053, 0.39891056]), array([0.3496102 , 0.3629617 , 0.34083677]), array([0.32607022, 0.38587592, 0.35525961]), array([0.19191793, 0.25275702, 0.33515516]), array([0.47272686, 0.40837079, 0.34218251]), array([0.3040727 , 0.26224712, 0.3890703 ]), array([0.30008117, 0.32285318, 0.39871217]), array([0.23419017, 0.38352744, 0.35418471]), array([0.35891006, 0.30984981, 0.39573756]), array([0.47986624, 0.31798773, 0.28156815]), array([0.26665275, 0.2829936 , 0.41306937]), array([0.37551826, 0.25699059, 0.37009496]), array([0.19260559, 0.23666399, 0.3920739 ]), array([0.42838029, 0.25238102, 0.33443302]), array([0.3187504 , 0.25969712, 0.37369899]), array([0.35505782, 0.28798638, 0.35327939]), array([0.24799177, 0.4442738 , 0.35864961]), array([0.23299177, 0.24366543, 0.30912317]), array([0.17930944, 0.26630175, 0.35006718]), array([0.33773089, 0.37009317, 0.36499906]), array([0.23734463, 0.21924722, 0.36963669]), array([0.1920668 , 0.20468487, 0.33009087]), array([0.3598286 , 0.24638025, 0.41308051]), array([0.33114727, 0.34612272, 0.36499183]), array([0.46637021, 0.31503762, 0.36270644]), array([0.40387483, 0.2107992 , 0.37669977]), array([0.24325003, 0.28963707, 0.39522785]), array([0.3069391 , 0.26743872, 0.36545819]), array([0.15040048, 0.34011869, 0.30411937]), array([0.22810418, 0.18611316, 0.37822326]), array([0.2831955 , 0.47945717, 0.35147055]), array([0.28996946, 0.28752643, 0.36070307]), array([0.30555674, 0.2561537 , 0.42807183]), array([0.2848247 , 0.28636344, 0.35491407]), array([0.33594718, 0.38285341, 0.33490481]), array([0.27150569, 0.31604809, 0.36326348]), array([0.17181878, 0.1701912 , 0.35880512]), array([0.37459296, 0.31552757, 0.35177802]), array([0.227506  , 0.19372036, 0.361658  ]), array([0.40817827, 0.26835471, 0.36129252]), array([0.23724304, 0.34618603, 0.39758382]), array([0.29488306, 0.28881528, 0.40402569]), array([0.26492323, 0.49344463, 0.37086023]), array([0.27085225, 0.3487291 , 0.39350177]), array([0.22510848, 0.34293758, 0.32320408]), array([0.30858642, 0.26257546, 0.36605613]), array([0.37161815, 0.36974081, 0.34319006]), array([0.35402753, 0.32968261, 0.39024631]), array([0.19418713, 0.1776602 , 0.42227402]), array([0.22266827, 0.30140507, 0.35442973]), array([0.28253716, 0.31371356, 0.35672037]), array([0.2251295 , 0.27483157, 0.35695248]), array([0.29996723, 0.31988707, 0.37335526]), array([0.36725531, 0.28928321, 0.35786821]), array([0.24786546, 0.28667747, 0.28897847]), array([0.32043973, 0.31417953, 0.37194908]), array([0.18082388, 0.3665133 , 0.32210587]), array([0.17689567, 0.30713796, 0.36183495]), array([0.37244636, 0.30146375, 0.30195097]), array([0.41759464, 0.34594153, 0.36589075]), array([0.23029387, 0.27373901, 0.36383043]), array([0.1916701 , 0.35735665, 0.36366443]), array([0.24842499, 0.24242424, 0.3910061 ]), array([0.35916414, 0.24950333, 0.28068323]), array([0.22799904, 0.3304417 , 0.35451503]), array([0.22882165, 0.39709803, 0.37274585]), array([0.25762889, 0.28189419, 0.34633901]), array([0.2674909 , 0.28165972, 0.39522785]), array([0.29960782, 0.19480254, 0.37772052]), array([0.20250949, 0.35915647, 0.32164194]), array([0.41848917, 0.36880432, 0.43889908]), array([0.31853785, 0.30296715, 0.34231037]), array([0.52361848, 0.29569753, 0.30671619]), array([0.27967088, 0.3505326 , 0.2950741 ]), array([0.3178536 , 0.25886775, 0.42442397]), array([0.20874592, 0.26464205, 0.35927993]), array([0.30111918, 0.21262912, 0.48236367]), array([0.37243238, 0.31208143, 0.39941065]), array([0.36709354, 0.43237843, 0.31519016]), array([0.3429803 , 0.25592639, 0.41066894]), array([0.12944003, 0.36666813, 0.37047208]), array([0.28086031, 0.32083614, 0.37934473]), array([0.19782568, 0.18523589, 0.27571297]), array([0.38403602, 0.39405183, 0.37164231]), array([0.21645543, 0.28148549, 0.3568734 ]), array([0.35056752, 0.26112184, 0.42904991])]\n",
            "400\n",
            "closest_vals_array =  [[0.39947727 0.33823837 0.3967953 ]\n",
            " [0.32987194 0.51257909 0.38383969]\n",
            " [0.23134791 0.42604355 0.38703422]\n",
            " ...\n",
            " [0.38403602 0.39405183 0.37164231]\n",
            " [0.21645543 0.28148549 0.3568734 ]\n",
            " [0.35056752 0.26112184 0.42904991]]\n",
            "closest_vals_array shape (400, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "error = mean_squared_error(closest_vals_array, y_pred)\n",
        "print(closest_vals_array.shape)\n",
        "print(y_pred.shape)\n",
        "print('MSE: %.3f' % error)\n",
        "print(y_pred)\n",
        "print(\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYKw_y06wA0N",
        "outputId": "34253f7e-935e-4457-90c8-226b92d27e6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(400, 3)\n",
            "(400, 3)\n",
            "MSE: 0.005\n",
            "[[0.34234032 0.3210212  0.3768671 ]\n",
            " [0.41124624 0.33555305 0.3908655 ]\n",
            " [0.25943807 0.28557402 0.3558829 ]\n",
            " ...\n",
            " [0.32449204 0.3254438  0.3751228 ]\n",
            " [0.25943807 0.28557402 0.3558829 ]\n",
            " [0.3919217  0.34053168 0.38902625]]\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Evaluation\n",
        "test_loss = model.evaluate(X_test, closest_vals_array)\n",
        "print(\"Test Loss:\", test_loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hgyLOytTwTRr",
        "outputId": "6c79a7f0-d3bf-4b5d-f820-9ed22e41179e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13/13 [==============================] - 0s 2ms/step - loss: 0.0048\n",
            "Test Loss: 0.004785528872162104\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_denorm_X = (y_pred[:, 0] * X_max)"
      ],
      "metadata": {
        "id": "J50Tb9bLwdoT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_denorm_X = np.reshape(y_pred_denorm_X, (-1,1))\n",
        "y_pred_denorm_X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T70gxPLnNB3r",
        "outputId": "53d122c8-18fc-4c5e-a83a-2c19c68510f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_denorm_Y = (y_pred[:, 1] * Y_max)"
      ],
      "metadata": {
        "id": "qBEsh6xqLLj1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_denorm_Y = np.reshape(y_pred_denorm_Y, (-1,1))"
      ],
      "metadata": {
        "id": "NDefdf7JODo9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_denorm_Z = (y_pred[:, 2] * Z_max)"
      ],
      "metadata": {
        "id": "mSXDRmyfLSHR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_denorm_Z = np.reshape(y_pred_denorm_Z, (-1,1))"
      ],
      "metadata": {
        "id": "opU_MkKyOHLs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine the y_pred_denorm's X,Y and Z coordinates\n",
        "y_pred_denorm = np.hstack((y_pred_denorm_X, y_pred_denorm_Y, y_pred_denorm_Z))  #array concatenation"
      ],
      "metadata": {
        "id": "Z1tJIUA0MAPz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_denorm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ih7AvOBEMO39",
        "outputId": "ad45c7ba-bc99-4134-d50d-c5e9c8a006c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.24502  , 2.7607825, 1.6582152],\n",
              "       [5.0994534, 2.8857563, 1.7198082],\n",
              "       [3.217032 , 2.4559367, 1.5658848],\n",
              "       ...,\n",
              "       [4.023701 , 2.798817 , 1.6505404],\n",
              "       [3.217032 , 2.4559367, 1.5658848],\n",
              "       [4.859829 , 2.9285727, 1.7117156]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_denorm.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9fz1Q13vNPV0",
        "outputId": "ebe5e391-242d-4fc0-f64a-e8a87ddc48d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "closest_vals_array_denorm_X = (closest_vals_array[:, 0] * X_max)"
      ],
      "metadata": {
        "id": "II41CPSNOwA3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "closest_vals_array_denorm_X = np.reshape(closest_vals_array_denorm_X, (-1,1))"
      ],
      "metadata": {
        "id": "5cZMcuaKQgk0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "closest_vals_array_denorm_Y = (closest_vals_array[:, 1] * Y_max)"
      ],
      "metadata": {
        "id": "eKLVzlh5O46-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "closest_vals_array_denorm_Y = np.reshape(closest_vals_array_denorm_Y, (-1,1))"
      ],
      "metadata": {
        "id": "nV39shxxQkGo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "closest_vals_array_denorm_Z = (closest_vals_array[:, 2] * Z_max)"
      ],
      "metadata": {
        "id": "aVb1vhIZO6wG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "closest_vals_array_denorm_Z = np.reshape(closest_vals_array_denorm_Z, (-1,1))"
      ],
      "metadata": {
        "id": "FfH-cXzqQlAH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine the closest_vals_array_denorm's X,Y and Z coordinates\n",
        "closest_vals_array_denorm = np.hstack((closest_vals_array_denorm_X, closest_vals_array_denorm_Y, closest_vals_array_denorm_Z))  #array concatenation"
      ],
      "metadata": {
        "id": "e9qaxaXzOc99"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(closest_vals_array_denorm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JxGqJao8QGwS",
        "outputId": "6455ccf1-0607-4d0a-9bdd-db3f2cc0b811"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[4.95351817 2.90885    1.7458993 ]\n",
            " [4.09041212 4.40818014 1.68889465]\n",
            " [2.86871409 3.66397449 1.70295055]\n",
            " ...\n",
            " [4.76204665 3.38884571 1.63522618]\n",
            " [2.68404739 2.42077519 1.57024294]\n",
            " [4.34703719 2.2456478  1.8878196 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "closest_vals_array_denorm.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uq1mFQoDQW4n",
        "outputId": "e3aed502-3980-4aa5-fcb8-409c23374173"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Calculate the loss for each coordinate separately\n",
        "# x_loss = ((closest_vals_array_denorm[:, 0] - y_pred_denorm[:, 0])**2)\n",
        "# y_loss = ((closest_vals_array_denorm[:, 1] - y_pred_denorm[:, 1])**2)\n",
        "# z_loss = ((closest_vals_array_denorm[:, 2] - y_pred_denorm[:, 2])**2)\n",
        "\n",
        "# print(x_loss)\n",
        "\n",
        "# # print(\"\\n difference in values: \", y_test[:, 0] - y_pred[:, 0])\n",
        "# print(\"\\n\")\n",
        "\n",
        "# print(x_loss)\n",
        "# print(x_loss.shape)\n",
        "\n",
        "\n",
        "# # Check if values in the ndarray are infinite for x_loss\n",
        "# is_inf = np.isinf(x_loss)\n",
        "\n",
        "# # Remove infinite values from the ndarray using boolean indexing\n",
        "# x_loss_new = x_loss[~is_inf]\n",
        "\n",
        "# print(\"\\nArray with Infinite Values:\")\n",
        "# print(x_loss[is_inf])\n",
        "\n",
        "# print(\"\\nCleaned ndarray (without Infinite Values):\")\n",
        "# print(x_loss_new)\n",
        "# print(x_loss_new.shape)\n",
        "\n",
        "# # np.delete(a, 0))\n",
        "\n",
        "# # Check if values in the ndarray are infinite for y_loss\n",
        "# is_inf = np.isinf(y_loss)\n",
        "\n",
        "# # Remove infinite values from the ndarray using boolean indexing\n",
        "# y_loss_new = y_loss[~is_inf]\n",
        "\n",
        "\n",
        "# # Check if values in the ndarray are infinite for z_loss\n",
        "# is_inf = np.isinf(z_loss)\n",
        "\n",
        "# # Remove infinite values from the ndarray using boolean indexing\n",
        "# z_loss_new = z_loss[~is_inf]\n"
      ],
      "metadata": {
        "id": "XQabo0IXQb96"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Calculate the sum of all values in each loss array\n",
        "# x_loss_sum = np.sum(x_loss_new)\n",
        "# y_loss_sum = np.sum(y_loss_new)\n",
        "# z_loss_sum = np.sum(z_loss_new)\n",
        "\n",
        "# x_loss_sum = (x_loss_sum**(1/2))\n",
        "# y_loss_sum = (y_loss_sum**(1/2))\n",
        "# z_loss_sum = (z_loss_sum**(1/2))\n",
        "\n",
        "# # Print the sum of losses for each coordinate\n",
        "# print(\"Sum of X Losses:\", x_loss_sum)\n",
        "# print(\"Sum of Y Losses:\", y_loss_sum)\n",
        "# print(\"Sum of Z Losses:\", z_loss_sum)"
      ],
      "metadata": {
        "id": "lz79rqa8RO8q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(min_distances_array)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6UPI4iwlRVti",
        "outputId": "896f2280-099e-44c3-f009-caf0bccfaa61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.03727977453029934"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.std(min_distances_array)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jm94RLdRWsp",
        "outputId": "025bcd8f-5547-40e0-998b-0436cc4d5151"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02521752841197387"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(min_distances_array)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GHJKyXMhRXwE",
        "outputId": "d05ed420-f87a-42c2-a45d-529874d9d56e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0006359237392087095"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_distances_array.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sv2JkVXERe2u",
        "outputId": "6a06369a-e5d1-4db0-c602-62d87cb3a44e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400,)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZmUtS6JFT83d",
        "outputId": "3860d3df-9dfa-437a-b1f1-227fe10c11c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "wf0aElktif8t",
        "outputId": "c228146c-ca48-4186-ece6-d221e4aa33ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             0         1         2         3         4         5\n",
              "0     0.645161  0.697674  0.681818  0.412186  0.312427  0.369605\n",
              "1     0.830645  0.744186  0.795455  0.582111  0.260747  0.345500\n",
              "2     0.419355  0.383721  0.659091  0.253177  0.208715  0.343434\n",
              "3     0.604839  0.604651  0.750000  0.389378  0.350951  0.377410\n",
              "4     0.419355  0.383721  0.659091  0.194526  0.213648  0.386364\n",
              "...        ...       ...       ...       ...       ...       ...\n",
              "1201  0.604839  0.604651  0.750000  0.443548  0.266385  0.337236\n",
              "1227  0.604839  0.604651  0.750000  0.161290  0.325581  0.345271\n",
              "1294  0.645161  0.697674  0.681818  0.330727  0.368804  0.383379\n",
              "1319  0.709677  0.616279  0.704545  0.216031  0.235612  0.258264\n",
              "1452  1.000000  1.000000  1.000000  0.517107  0.286587  0.519743\n",
              "\n",
              "[400 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-91715fc1-c23a-4ba6-9c4f-9f444a4e5cec\" class=\"colab-df-container\">\n",
              "    <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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.645161</td>\n",
              "      <td>0.697674</td>\n",
              "      <td>0.681818</td>\n",
              "      <td>0.412186</td>\n",
              "      <td>0.312427</td>\n",
              "      <td>0.369605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.830645</td>\n",
              "      <td>0.744186</td>\n",
              "      <td>0.795455</td>\n",
              "      <td>0.582111</td>\n",
              "      <td>0.260747</td>\n",
              "      <td>0.345500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.419355</td>\n",
              "      <td>0.383721</td>\n",
              "      <td>0.659091</td>\n",
              "      <td>0.253177</td>\n",
              "      <td>0.208715</td>\n",
              "      <td>0.343434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.604839</td>\n",
              "      <td>0.604651</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.389378</td>\n",
              "      <td>0.350951</td>\n",
              "      <td>0.377410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.419355</td>\n",
              "      <td>0.383721</td>\n",
              "      <td>0.659091</td>\n",
              "      <td>0.194526</td>\n",
              "      <td>0.213648</td>\n",
              "      <td>0.386364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1201</th>\n",
              "      <td>0.604839</td>\n",
              "      <td>0.604651</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.443548</td>\n",
              "      <td>0.266385</td>\n",
              "      <td>0.337236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1227</th>\n",
              "      <td>0.604839</td>\n",
              "      <td>0.604651</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.161290</td>\n",
              "      <td>0.325581</td>\n",
              "      <td>0.345271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1294</th>\n",
              "      <td>0.645161</td>\n",
              "      <td>0.697674</td>\n",
              "      <td>0.681818</td>\n",
              "      <td>0.330727</td>\n",
              "      <td>0.368804</td>\n",
              "      <td>0.383379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1319</th>\n",
              "      <td>0.709677</td>\n",
              "      <td>0.616279</td>\n",
              "      <td>0.704545</td>\n",
              "      <td>0.216031</td>\n",
              "      <td>0.235612</td>\n",
              "      <td>0.258264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1452</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.517107</td>\n",
              "      <td>0.286587</td>\n",
              "      <td>0.519743</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 6 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "  <div id=\"id_10406d04-fa9f-413a-b2f3-82fec9e90575\">\n",
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              "      .colab-df-generate {\n",
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              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_10406d04-fa9f-413a-b2f3-82fec9e90575 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X_test');\n",
              "      }\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "room_X_test = (X_test.iloc[:, 0] * X_max)"
      ],
      "metadata": {
        "id": "XOq7Q3TtRers"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(room_X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Q1AclhyU1Qu",
        "outputId": "7027b9cd-236c-444c-d489-60a50a4c0895"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "room_X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gKBp3nbQU6BO",
        "outputId": "29d07cba-9710-45c9-80fb-aae2e5294d80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400,)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "room_X_test = room_X_test.values.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "ju24K-0lUlOe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "room_X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4JaGD3dVaLi",
        "outputId": "1781c2e4-9fb5-4669-ee9d-afb0b8cdc7f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "room_Y_test = (X_test.iloc[:, 1] * Y_max)"
      ],
      "metadata": {
        "id": "WpDTEyRxRejT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "room_Y_test = room_Y_test.values.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "huKk2xLIUpKR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "room_Z_test = (X_test.iloc[:, 2] * Z_max)"
      ],
      "metadata": {
        "id": "GzxPfz_XTrmo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "room_Z_test = room_Z_test.values.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "0cj57-YbUp0b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "room_Z_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZpYZww2nUICo",
        "outputId": "40fe2b79-1b8a-485d-e12b-88c6c602980e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(room_Z_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m6my-tUNfvLL",
        "outputId": "519ff8a7-5c47-4292-a93d-e3d7b5ca589b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "speaker_X_test = (X_test.iloc[:, 3] * X_max)\n",
        "speaker_X_test = speaker_X_test.values.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "AEYxLY1qkF5Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "speaker_Y_test = (X_test.iloc[:, 4] * Y_max)\n",
        "speaker_Y_test = speaker_Y_test.values.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "vCQER6rfkFtI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "speaker_Z_test = (X_test.iloc[:, 5] * Z_max)\n",
        "speaker_Z_test = speaker_Z_test.values.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "mhrXXeavkFdt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge the three room and speaker column arrays horizontally\n",
        "room_speaker_denorm = np.concatenate((room_X_test, room_Y_test, room_Z_test, speaker_X_test, speaker_Y_test, speaker_Z_test), axis=1)"
      ],
      "metadata": {
        "id": "QditAzdUUK0w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "room_speaker_denorm.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0SB-rivXW0TE",
        "outputId": "96ff55d4-a6c5-430b-9aec-efaeb37f3d72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert array to dataframe\n",
        "room_speaker_denorm_df = pd.DataFrame(room_speaker_denorm)"
      ],
      "metadata": {
        "id": "GPfwz2uWgOzG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "room_speaker_denorm_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "9EJ_6szdggm7",
        "outputId": "65c3f61a-7160-4a17-89df-2051f223f52b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        0    1    2         3         4         5\n",
              "0     8.0  6.0  3.0  5.111111  2.686869  1.626263\n",
              "1    10.3  6.4  3.5  7.218182  2.242424  1.520202\n",
              "2     5.2  3.3  2.9  3.139394  1.794949  1.511111\n",
              "3     7.5  5.2  3.3  4.828283  3.018182  1.660606\n",
              "4     5.2  3.3  2.9  2.412121  1.837374  1.700000\n",
              "..    ...  ...  ...       ...       ...       ...\n",
              "395   7.5  5.2  3.3  5.500000  2.290909  1.483838\n",
              "396   7.5  5.2  3.3  2.000000  2.800000  1.519192\n",
              "397   8.0  6.0  3.0  4.101010  3.171717  1.686869\n",
              "398   8.8  5.3  3.1  2.678788  2.026263  1.136364\n",
              "399  12.4  8.6  4.4  6.412121  2.464646  2.286869\n",
              "\n",
              "[400 rows x 6 columns]"
            ],
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              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.111111</td>\n",
              "      <td>2.686869</td>\n",
              "      <td>1.626263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.3</td>\n",
              "      <td>6.4</td>\n",
              "      <td>3.5</td>\n",
              "      <td>7.218182</td>\n",
              "      <td>2.242424</td>\n",
              "      <td>1.520202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.2</td>\n",
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              "      <td>3.139394</td>\n",
              "      <td>1.794949</td>\n",
              "      <td>1.511111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>3.3</td>\n",
              "      <td>4.828283</td>\n",
              "      <td>3.018182</td>\n",
              "      <td>1.660606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.3</td>\n",
              "      <td>2.9</td>\n",
              "      <td>2.412121</td>\n",
              "      <td>1.837374</td>\n",
              "      <td>1.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>7.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>2.290909</td>\n",
              "      <td>1.483838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>7.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>3.3</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.519192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.101010</td>\n",
              "      <td>3.171717</td>\n",
              "      <td>1.686869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>8.8</td>\n",
              "      <td>5.3</td>\n",
              "      <td>3.1</td>\n",
              "      <td>2.678788</td>\n",
              "      <td>2.026263</td>\n",
              "      <td>1.136364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>12.4</td>\n",
              "      <td>8.6</td>\n",
              "      <td>4.4</td>\n",
              "      <td>6.412121</td>\n",
              "      <td>2.464646</td>\n",
              "      <td>2.286869</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 6 columns</p>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f2a43db6-6148-489d-a97a-74b145ae3a1a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-f2a43db6-6148-489d-a97a-74b145ae3a1a');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7d89fa2b-a16a-4d1e-acf0-2b5da7b1b2eb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_72448441-b5aa-4f90-8ee5-4b2a052e7aa3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('room_speaker_denorm_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_72448441-b5aa-4f90-8ee5-4b2a052e7aa3 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('room_speaker_denorm_df');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# concatenate y_pred and closest_vals_array (both denormalized)\n",
        "y_test_pred = np.concatenate((closest_vals_array_denorm, y_pred_denorm), axis=1)"
      ],
      "metadata": {
        "id": "ujjVnefbgilm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5N5F6xnhm-T",
        "outputId": "e2f59e59-3b4f-4956-d71c-e43e42ba6c69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.95351817, 2.90885   , 1.7458993 , 4.24501991, 2.76078248,\n",
              "        1.65821517],\n",
              "       [4.09041212, 4.40818014, 1.68889465, 5.09945345, 2.88575625,\n",
              "        1.71980822],\n",
              "       [2.86871409, 3.66397449, 1.70295055, 3.21703196, 2.45593667,\n",
              "        1.56588483],\n",
              "       ...,\n",
              "       [4.76204665, 3.38884571, 1.63522618, 4.02370119, 2.79881692,\n",
              "        1.65054035],\n",
              "       [2.68404739, 2.42077519, 1.57024294, 3.21703196, 2.45593667,\n",
              "        1.56588483],\n",
              "       [4.34703719, 2.2456478 , 1.8878196 , 4.85982895, 2.92857265,\n",
              "        1.71171558]])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_pred_df = pd.DataFrame(y_test_pred)\n",
        "y_test_pred_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "a1A8J0-ghqL7",
        "outputId": "e0947092-ac3c-4744-935f-fe08a0ca1288"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            0         1         2         3         4         5\n",
              "0    4.953518  2.908850  1.745899  4.245020  2.760782  1.658215\n",
              "1    4.090412  4.408180  1.688895  5.099453  2.885756  1.719808\n",
              "2    2.868714  3.663974  1.702951  3.217032  2.455937  1.565885\n",
              "3    4.610531  3.151472  1.549368  4.022144  2.696714  1.638437\n",
              "4    3.427015  2.316509  1.675179  3.217032  2.455937  1.565885\n",
              "..        ...       ...       ...       ...       ...       ...\n",
              "395  3.482668  2.759191  1.669117  4.287801  2.631851  1.645359\n",
              "396  2.453038  1.593029  1.213137  3.217032  2.455937  1.565885\n",
              "397  4.762047  3.388846  1.635226  4.023701  2.798817  1.650540\n",
              "398  2.684047  2.420775  1.570243  3.217032  2.455937  1.565885\n",
              "399  4.347037  2.245648  1.887820  4.859829  2.928573  1.711716\n",
              "\n",
              "[400 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7c45afd9-29ac-4b0e-af6d-d7ed8a67d322\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.953518</td>\n",
              "      <td>2.908850</td>\n",
              "      <td>1.745899</td>\n",
              "      <td>4.245020</td>\n",
              "      <td>2.760782</td>\n",
              "      <td>1.658215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.090412</td>\n",
              "      <td>4.408180</td>\n",
              "      <td>1.688895</td>\n",
              "      <td>5.099453</td>\n",
              "      <td>2.885756</td>\n",
              "      <td>1.719808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.868714</td>\n",
              "      <td>3.663974</td>\n",
              "      <td>1.702951</td>\n",
              "      <td>3.217032</td>\n",
              "      <td>2.455937</td>\n",
              "      <td>1.565885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.610531</td>\n",
              "      <td>3.151472</td>\n",
              "      <td>1.549368</td>\n",
              "      <td>4.022144</td>\n",
              "      <td>2.696714</td>\n",
              "      <td>1.638437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.427015</td>\n",
              "      <td>2.316509</td>\n",
              "      <td>1.675179</td>\n",
              "      <td>3.217032</td>\n",
              "      <td>2.455937</td>\n",
              "      <td>1.565885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>3.482668</td>\n",
              "      <td>2.759191</td>\n",
              "      <td>1.669117</td>\n",
              "      <td>4.287801</td>\n",
              "      <td>2.631851</td>\n",
              "      <td>1.645359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>2.453038</td>\n",
              "      <td>1.593029</td>\n",
              "      <td>1.213137</td>\n",
              "      <td>3.217032</td>\n",
              "      <td>2.455937</td>\n",
              "      <td>1.565885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>4.762047</td>\n",
              "      <td>3.388846</td>\n",
              "      <td>1.635226</td>\n",
              "      <td>4.023701</td>\n",
              "      <td>2.798817</td>\n",
              "      <td>1.650540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>2.684047</td>\n",
              "      <td>2.420775</td>\n",
              "      <td>1.570243</td>\n",
              "      <td>3.217032</td>\n",
              "      <td>2.455937</td>\n",
              "      <td>1.565885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>4.347037</td>\n",
              "      <td>2.245648</td>\n",
              "      <td>1.887820</td>\n",
              "      <td>4.859829</td>\n",
              "      <td>2.928573</td>\n",
              "      <td>1.711716</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 6 columns</p>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-7c45afd9-29ac-4b0e-af6d-d7ed8a67d322 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bdf61bdf-f27c-42a8-9089-b6f667763ac9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
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              "\n",
              "  <div id=\"id_b6a3d3b4-8fc2-485e-9ea3-b57c87ef79c4\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('y_test_pred_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b6a3d3b4-8fc2-485e-9ea3-b57c87ef79c4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('y_test_pred_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge the three DataFrames horizontally\n",
        "merged_df = pd.concat([room_speaker_denorm_df, y_test_pred_df], axis=1)"
      ],
      "metadata": {
        "id": "6NXbGXxWh3_l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IPIBBS17nEa0",
        "outputId": "ec9fdd5c-0b07-4af3-fab2-fdc60139103a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "ei5pn1J0nHCr",
        "outputId": "85f89174-6f86-4043-b773-76973b7370d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        0    1    2         3         4         5         0         1  \\\n",
              "0     8.0  6.0  3.0  5.111111  2.686869  1.626263  4.953518  2.908850   \n",
              "1    10.3  6.4  3.5  7.218182  2.242424  1.520202  4.090412  4.408180   \n",
              "2     5.2  3.3  2.9  3.139394  1.794949  1.511111  2.868714  3.663974   \n",
              "3     7.5  5.2  3.3  4.828283  3.018182  1.660606  4.610531  3.151472   \n",
              "4     5.2  3.3  2.9  2.412121  1.837374  1.700000  3.427015  2.316509   \n",
              "..    ...  ...  ...       ...       ...       ...       ...       ...   \n",
              "395   7.5  5.2  3.3  5.500000  2.290909  1.483838  3.482668  2.759191   \n",
              "396   7.5  5.2  3.3  2.000000  2.800000  1.519192  2.453038  1.593029   \n",
              "397   8.0  6.0  3.0  4.101010  3.171717  1.686869  4.762047  3.388846   \n",
              "398   8.8  5.3  3.1  2.678788  2.026263  1.136364  2.684047  2.420775   \n",
              "399  12.4  8.6  4.4  6.412121  2.464646  2.286869  4.347037  2.245648   \n",
              "\n",
              "            2         3         4         5  \n",
              "0    1.745899  4.245020  2.760782  1.658215  \n",
              "1    1.688895  5.099453  2.885756  1.719808  \n",
              "2    1.702951  3.217032  2.455937  1.565885  \n",
              "3    1.549368  4.022144  2.696714  1.638437  \n",
              "4    1.675179  3.217032  2.455937  1.565885  \n",
              "..        ...       ...       ...       ...  \n",
              "395  1.669117  4.287801  2.631851  1.645359  \n",
              "396  1.213137  3.217032  2.455937  1.565885  \n",
              "397  1.635226  4.023701  2.798817  1.650540  \n",
              "398  1.570243  3.217032  2.455937  1.565885  \n",
              "399  1.887820  4.859829  2.928573  1.711716  \n",
              "\n",
              "[400 rows x 12 columns]"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>0</th>\n",
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              "      <th>2</th>\n",
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              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.111111</td>\n",
              "      <td>2.686869</td>\n",
              "      <td>1.626263</td>\n",
              "      <td>4.953518</td>\n",
              "      <td>2.908850</td>\n",
              "      <td>1.745899</td>\n",
              "      <td>4.245020</td>\n",
              "      <td>2.760782</td>\n",
              "      <td>1.658215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.3</td>\n",
              "      <td>6.4</td>\n",
              "      <td>3.5</td>\n",
              "      <td>7.218182</td>\n",
              "      <td>2.242424</td>\n",
              "      <td>1.520202</td>\n",
              "      <td>4.090412</td>\n",
              "      <td>4.408180</td>\n",
              "      <td>1.688895</td>\n",
              "      <td>5.099453</td>\n",
              "      <td>2.885756</td>\n",
              "      <td>1.719808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.3</td>\n",
              "      <td>2.9</td>\n",
              "      <td>3.139394</td>\n",
              "      <td>1.794949</td>\n",
              "      <td>1.511111</td>\n",
              "      <td>2.868714</td>\n",
              "      <td>3.663974</td>\n",
              "      <td>1.702951</td>\n",
              "      <td>3.217032</td>\n",
              "      <td>2.455937</td>\n",
              "      <td>1.565885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>3.3</td>\n",
              "      <td>4.828283</td>\n",
              "      <td>3.018182</td>\n",
              "      <td>1.660606</td>\n",
              "      <td>4.610531</td>\n",
              "      <td>3.151472</td>\n",
              "      <td>1.549368</td>\n",
              "      <td>4.022144</td>\n",
              "      <td>2.696714</td>\n",
              "      <td>1.638437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.3</td>\n",
              "      <td>2.9</td>\n",
              "      <td>2.412121</td>\n",
              "      <td>1.837374</td>\n",
              "      <td>1.700000</td>\n",
              "      <td>3.427015</td>\n",
              "      <td>2.316509</td>\n",
              "      <td>1.675179</td>\n",
              "      <td>3.217032</td>\n",
              "      <td>2.455937</td>\n",
              "      <td>1.565885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>7.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>2.290909</td>\n",
              "      <td>1.483838</td>\n",
              "      <td>3.482668</td>\n",
              "      <td>2.759191</td>\n",
              "      <td>1.669117</td>\n",
              "      <td>4.287801</td>\n",
              "      <td>2.631851</td>\n",
              "      <td>1.645359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>7.5</td>\n",
              "      <td>5.2</td>\n",
              "      <td>3.3</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.519192</td>\n",
              "      <td>2.453038</td>\n",
              "      <td>1.593029</td>\n",
              "      <td>1.213137</td>\n",
              "      <td>3.217032</td>\n",
              "      <td>2.455937</td>\n",
              "      <td>1.565885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.101010</td>\n",
              "      <td>3.171717</td>\n",
              "      <td>1.686869</td>\n",
              "      <td>4.762047</td>\n",
              "      <td>3.388846</td>\n",
              "      <td>1.635226</td>\n",
              "      <td>4.023701</td>\n",
              "      <td>2.798817</td>\n",
              "      <td>1.650540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>8.8</td>\n",
              "      <td>5.3</td>\n",
              "      <td>3.1</td>\n",
              "      <td>2.678788</td>\n",
              "      <td>2.026263</td>\n",
              "      <td>1.136364</td>\n",
              "      <td>2.684047</td>\n",
              "      <td>2.420775</td>\n",
              "      <td>1.570243</td>\n",
              "      <td>3.217032</td>\n",
              "      <td>2.455937</td>\n",
              "      <td>1.565885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>12.4</td>\n",
              "      <td>8.6</td>\n",
              "      <td>4.4</td>\n",
              "      <td>6.412121</td>\n",
              "      <td>2.464646</td>\n",
              "      <td>2.286869</td>\n",
              "      <td>4.347037</td>\n",
              "      <td>2.245648</td>\n",
              "      <td>1.887820</td>\n",
              "      <td>4.859829</td>\n",
              "      <td>2.928573</td>\n",
              "      <td>1.711716</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('merged_df')\"\n",
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              "\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the DataFrame to a CSV file with a tab ('\\t') as the delimiter\n",
        "merged_df.to_csv('Final60_40_dec.csv', index=False)"
      ],
      "metadata": {
        "id": "qfU5m0GsnK3g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Read STOI values from the CSV file\n",
        "fig = pd.read_csv('/content/Averaged_Test_and_Predicted_STOI_6040.csv', header=None)\n",
        "# y_test_stoi = fig.iloc[:, 0].values\n",
        "# y_pred_stoi = fig.iloc[:, 1].values\n",
        "y_test_stoi = fig.iloc[25:50, 0].values\n",
        "y_pred_stoi = fig.iloc[25:50, 1].values"
      ],
      "metadata": {
        "id": "NlWXK6_Opoxa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create an array of indices for x-axis (200 rows)\n",
        "x_indices = np.arange(200)\n",
        "x_indi = x_indices[25:50]"
      ],
      "metadata": {
        "id": "OfFMpVsuYvef"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_stoi.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XdYpBujUbO65",
        "outputId": "cc4ca24f-f14b-42a0-eb75-23119bc114d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25,)"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "feixPZ3Ibczw",
        "outputId": "508bf880-87a4-4828-caa3-869d02060c89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the plot\n",
        "plt.figure(figsize=(9, 6))\n",
        "# Customize the plot\n",
        "plt.xlabel('Total number of positions', fontsize=12)\n",
        "plt.ylabel('STOI Values', fontsize=12)\n",
        "plt.title('STOI Comparison', fontsize=14)\n",
        "plt.xticks(range(25, 50, 5), rotation=0, fontsize=13)\n",
        "plt.yticks(fontsize=13)\n",
        "plt.ylim(0, 1)\n",
        "# plt.xlim(25,50)\n",
        "plt.plot(x_indi, y_pred_stoi, label='y_pred STOI', color='blue', linewidth=2)\n",
        "plt.plot(x_indi, y_test_stoi, label='y_test STOI', color='red', linewidth=2)\n",
        "plt.grid(True)\n",
        "# plt.legend(fontsize=10)\n",
        "plt.figure(figsize=(12, 6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "09CySPCXYvWK",
        "outputId": "756db8e8-39c2-4b8e-f6ce-a85d75201e3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/Final60_40.csv')\n",
        "sp_coordinates = data.iloc[25:50, 3:6].values #1:201\n",
        "y_tst_coordinates = data.iloc[25:50, 6:9].values\n",
        "y_prd_coordinates = data.iloc[25:50, 9:12].values"
      ],
      "metadata": {
        "id": "7SE6Zz3IY2Q6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate distances between speaker and y_pred/y_test\n",
        "distances_y_pred = np.linalg.norm(sp_coordinates - y_prd_coordinates, axis=1)\n",
        "distances_y_test = np.linalg.norm(sp_coordinates - y_tst_coordinates, axis=1)"
      ],
      "metadata": {
        "id": "saxAqo_cYvNe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "distances_y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iBI8OU6yZD4Q",
        "outputId": "6bf24987-0532-4fae-fc4e-cec0fa329603"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25,)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the plot\n",
        "plt.figure(figsize=(9, 6))\n",
        "# Customize the plot\n",
        "plt.xlabel('Total number of positions', fontsize=12)\n",
        "plt.ylabel('Distance from the Speaker', fontsize=12)\n",
        "plt.title('Distance Comparison', fontsize=14)\n",
        "plt.xticks(range(25, 50, 5), rotation=0, fontsize=13)\n",
        "plt.yticks(fontsize=13)\n",
        "# plt.ylim(0, 1)\n",
        "# plt.xlim(25,50)\n",
        "plt.plot(x_indi, distances_y_pred, label='Distance to y_pred', color='blue', linewidth=2)\n",
        "plt.plot(x_indi, distances_y_test, label='Distance to y_test', color='red', linewidth=2)\n",
        "plt.grid(True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "vVEE09q-Y9ct",
        "outputId": "8dfabe75-84ed-40ad-c38a-b695d4aeb6f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sy8OQFX3bkfZ"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}