unsupervised_learning_assignment.ipynb

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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/19PA1A05B3/MACHINE-LEARNING/blob/master/unsupervised_learning_assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "fb758fbaa68c09ce13b31fd6982499c2212de2a1",
        "id": "IgqFvDHkqiT8",
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      },
      "source": [
        "# <center>Assignment: Unsupervised learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "19ed99767bc346b8cabd9c285c46d78341b68ac0",
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      "source": [
        "In this task, we will look at how data dimensionality reduction and clustering methods work. At the same time, we'll practice solving classification task again.\n",
        "\n",
        "We will work with the [Samsung Human Activity Recognition](https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones) dataset. Download the data [here](https://drive.google.com/file/d/14RukQ0ylM2GCdViUHBBjZ2imCaYcjlux/view?usp=sharing). The data comes from accelerometers and gyros of Samsung Galaxy S3 mobile phones ( you can find more info about the features using the link above), the type of activity of a person with a phone in his/her pocket is also known – whether he/she walked, stood, lay, sat or walked up or down the stairs.\n",
        "\n",
        "First, we pretend that the type of activity is unknown to us, and we will try to cluster people purely on the basis of available features. Then we solve the problem of determining the type of physical activity as a classification problem.\n",
        "\n",
        "Fill the code where needed (\"Your code is here\") and answer the questions in the associated quiz on Edmodo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "49d35cc6431c8053964544de63bb91e638db1fa9",
        "id": "ce-qNEzWqiT9",
        "colab_type": "code",
        "outputId": "cb53a008-5cc2-4514-f93e-f51c5c631f18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "%matplotlib inline\n",
        "from matplotlib import pyplot as plt\n",
        "plt.style.use(['seaborn-darkgrid'])\n",
        "plt.rcParams['figure.figsize'] = (12, 9)\n",
        "plt.rcParams['font.family'] = 'DejaVu Sans'\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.cluster import KMeans, AgglomerativeClustering, SpectralClustering\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "RANDOM_STATE = 17"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OLO6D6QnjmFs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test=np.loadtxt(\"samsung_test.txt\")\n",
        "X_train=np.loadtxt(\"samsung_train.txt\")\n",
        "Y_train=np.loadtxt(\"samsung_train_labels.txt\").astype(int)\n",
        "Y_test=np.loadtxt(\"samsung_test_labels.txt\").astype(int)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "98b267f6cfd624a0458dabdde5525b27970978bc",
        "id": "br9P55dOqiUK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Checking dimensions\n",
        "assert(X_train.shape == (7352, 561) and Y_train.shape == (7352,))\n",
        "assert(X_test.shape == (2947, 561) and Y_test.shape == (2947,))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "6ae636fe849d7d67db976feaa7ca2e25ba7c3795",
        "id": "IoEUycunqiUQ",
        "colab_type": "text"
      },
      "source": [
        "For clustering, we do not need a target vector, so we'll work with the combination of training and test samples. Merge `X_train` with `X_test`, and `y_train` with `y_test`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxniBvyetymA",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
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      "source": [
        "x=np.concatenate((X_train,X_test),axis=0)\n",
        "y=np.concatenate((Y_train,Y_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 5, 5, ..., 2, 2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "77c4ccbe64c015a09874cff61bf751b0e7917ef4",
        "id": "5_eyDz20qiUV",
        "colab_type": "text"
      },
      "source": [
        "Define the number of unique values of the labels of the target class."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "5a92a3783a446acf928972d3ec418f29a0cff6e0",
        "id": "0iKV6JPcqiUW",
        "colab_type": "code",
        "outputId": "2d5ac743-acd4-4101-e7fb-9efc3fa0c697",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
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      },
      "source": [
        "np.unique(y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5, 6])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
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    },
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        "id": "P82gwRN0qiUb",
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      "source": [
        " n_classes = np.unique(y).size"
      ],
      "execution_count": 0,
      "outputs": []
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        "id": "PRu7RmeWzibf",
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          "base_uri": "https://localhost:8080/",
          "height": 243
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      },
      "source": [
        "x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.28858451, -0.02029417, -0.13290514, ..., -0.84124676,\n",
              "         0.17994061, -0.05862692],\n",
              "       [ 0.27841883, -0.01641057, -0.12352019, ..., -0.8447876 ,\n",
              "         0.18028889, -0.05431672],\n",
              "       [ 0.27965306, -0.01946716, -0.11346169, ..., -0.84893347,\n",
              "         0.18063731, -0.04911782],\n",
              "       ...,\n",
              "       [ 0.34996609,  0.03007744, -0.11578796, ..., -0.65535684,\n",
              "         0.27447878,  0.18118355],\n",
              "       [ 0.23759383,  0.01846687, -0.09649893, ..., -0.65971859,\n",
              "         0.26478161,  0.18756291],\n",
              "       [ 0.15362719, -0.01843651, -0.13701846, ..., -0.66008023,\n",
              "         0.26393619,  0.1881034 ]])"
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    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "3c878aa4cc63242a33b9d030efba7623bd8a5d2c",
        "id": "aw7irEPBqiUe",
        "colab_type": "text"
      },
      "source": [
        "[These labels correspond to:](https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.names)\n",
        "- 1 – walking\n",
        "- 2 – walking upstairs\n",
        "- 3 – walking downstairs\n",
        "- 4 – sitting\n",
        "- 5 – standing\n",
        "- 6 – laying down"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "f2037a9432194f9a3d2f390ca2ad49a1ac1a6192",
        "id": "qJCzOWzPqiUe",
        "colab_type": "text"
      },
      "source": [
        "Scale the sample using `StandardScaler` with default parameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "70461dfacab8bd090d951e6114beaa40aa293919",
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        }
      },
      "source": [
        "sad=StandardScaler()\n",
        "xstd=sad.fit_transform(x)\n",
        "xstd"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2.10533854e-01, -6.87026385e-02, -4.52194533e-01, ...,\n",
              "        -6.74432366e-01,  3.82007658e-01, -1.61505480e-02],\n",
              "       [ 6.02084832e-02,  3.59023537e-02, -2.75221885e-01, ...,\n",
              "        -6.81359801e-01,  3.83147865e-01, -1.20627169e-04],\n",
              "       [ 7.84597052e-02, -4.64269585e-02, -8.55480582e-02, ...,\n",
              "        -6.89470939e-01,  3.84288531e-01,  1.92144041e-02],\n",
              "       ...,\n",
              "       [ 1.11821625e+00,  1.28805864e+00, -1.29414692e-01, ...,\n",
              "        -3.10750224e-01,  6.91509049e-01,  8.75719123e-01],\n",
              "       [-5.43492722e-01,  9.75327452e-01,  2.34319837e-01, ...,\n",
              "        -3.19283719e-01,  6.59762215e-01,  8.99444350e-01],\n",
              "       [-1.78515253e+00, -1.86663622e-02, -5.29759691e-01, ...,\n",
              "        -3.19991245e-01,  6.56994458e-01,  9.01454465e-01]])"
            ]
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          "execution_count": 31
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      },
      "source": [
        "\n",
        "plt.scatter(xstd[:,0],xstd[:,1] , c=y, s=20, cmap='viridis');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "b7249210501c3396bddbf57cea47ac01a0d85170",
        "id": "5anrqpOZqiUi",
        "colab_type": "text"
      },
      "source": [
        "Reduce the number of dimensions using PCA, leaving as many components as necessary to explain at least 90% of the variance of the original (scaled) data. Use the scaled dataset and fix `random_state` (RANDOM_STATE constant)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "2fc8993e03857d93b59e5e1e5800f494b23239be",
        "id": "B-6HFUHVqiUj",
        "colab_type": "code",
        "outputId": "aca0c899-8d06-4664-f59c-4daef33f3f94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        }
      },
      "source": [
        "pca=PCA(random_state=17).fit(xstd)\n",
        "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
        "plt.xlabel('number of components')\n",
        "plt.ylabel('cumulative explained variance');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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taXUkAAAASRL7dqFbaGgN6bY/fq6vqpr08KWnUZgBAEC3QmmG5Rr9Id32p7bC/NClp+lbp7IkAwAAdC8sz4ClWoNh/Z9X1mvz3kY9NP00jWMNMwAA6IaYaYZlQuGI7lm6UWt312v+lGEaX0RhBgAA3ROlGZaIGIbuf3OzPtperXsmFrGtHAAA6NYozYg7wzD0H+9u1VubqvTjbw3ixiUAAKDbozQj7v777zv08po9uuabA3Qdt8YGAAA2QGlGXP3183L999936pLTfbp9/ClyuVxWRwIAADBFaUbcfLy9Wgve/krnDOqheycNoTADAADboDQjLjZXNmru0o0a3DtDv54+Ql4P/9UDAAD2QXNBzFU2+HXHK+uVnerVb2aOVEYy24MDAAB7ob0gplqDYf38rxvUEgxrydVnKS8zxepIAAAAx4zSjJgxDEPz3/pSmyob9eiM01XUO8PqSAAAAMeF5RmImf/5x069vblKt11wii7g9tgAAMDGKM2IiXe/2qdFH+3Q1NPyde2YAVbHAQAAOCGUZpx0m/c26v++vkkj+2Zp3qShbC0HAABsj9KMk6q6OaA7/7JB2alePXLpaUrx8l8xAABgf1wIiJMmFDE077WNqmkJ6r+vOlO92SkDAAAkCKYBcdI89WGp/lVWp7kTh2iEL8vqOAAAACcNpRknxd++2qdnPivTzDP6atrpPqvjAAAAnFSUZpywnTUtuv/NzRrhy9RPLxxsdRwAAICTjtKME+IPRXTP0i/kdbv06+lc+AcAABITDQcn5IkPtuurqiaVXDxM/XJSrY4DAAAQE5RmHLcPt+3X/67are+M7qdx3PEPAAAkMEozjsu+Rr9+8eaXGpKXoTnjTrU6DgAAQExRmnHMIoahkjc2qyUY1i+njWAdMwAASHgxbTsrV67U5MmTNWnSJC1evPiI47t379YPfvADTZ8+Xddee60qKipiGQcnybOf7dJnO2t154WDdUqvdKvjAAAAxFzMSnM4HNb8+fO1ZMkSLVu2TK+99pq2bNly2DkPPfSQZsyYoaVLl+rHP/6xHn300VjFwUmyqbJBT31UqolDe+uyUX2sjgMAABAXMSvN69atU2FhoQoKCpScnKxp06ZpxYoVh52zdetWnXPOOZKkc84554jj6F78oYhK3tisnulJumfiELlcLqsjAQAAxEXMSnNlZaX69Dk4E+nz+VRZWXnYOcOHD9fy5cslSW+//baamppUU1MTq0g4QU99WKrt+5v175OHKictyeo4AAAAceO18sPvuusuPfDAA3rllVd09tlny+fzyePxHHFeZmaKvN4jX481j8et3FzW7ErSp6XV+sOqXbp6TIGmnDXA6jhRMWb2wnjZD2NmP4yZ/TBm3VPMSrPP5zvswr7Kykr5fL4jznniiSckSU1NTVq+fLmys7OPeK/GRn+sYnYqNzddtbXNlnx2d9IUCOnnL69V/5xU3XzOwG79nTBm9sJ42Q9jZj+Mmf0wZtbJy8uKeixmyzNGjRql0tJSlZWVKRAIaNmyZSouLj7snOrqakUiEUnS4sWLNWvWrFjFwQn4zd+2qbzer/svHqb05PjP+AMAAFgtZqXZ6/WqpKREN954o6ZOnaopU6ZoyJAhWrhwYccFf59++qkuvvhiTZ48Wfv27dMtt9wSqzg4Th9tr9ZfPq/QtWMG6Mz+OVbHAQAAsITLMAzD6hBmqqoaLPlcp//1SFMgpO/87l9KT/boue99Q8k2uImJ08fMbhgv+2HM7Icxsx/GzDqWLM+A/T31Yan2Nvh130VDbVGYAQAAYoUmhKNat6deL63eoytH99MZ/Y68OBMAAMBJKM04QjAc0YPLv1R+Vopu+dYgq+MAAABYjtKMI/zu0zJt39+seyYWKSPZ0q28AQAAugVKMw6zbX+T/ueTnZo8PE/fOrWX1XEAAAC6BUozOkQMQwve/koZyR799MLBVscBAADoNijN6PD6F5Vas7tePxl3qnqmJ1sdBwAAoNugNEOSVN8a1H+9v12j+mbrkpE+818AAABwEK7ygqS2PZnrWoN6fOIouV0uq+MAAAB0K8w0QxsrG/SnteW64qx+GpafaXUcAACAbofS7HARw9DDK7aoR3qSbj5/kNVxAAAAuiVKs8O9+nmF1pc36PbxpyozhdU6AAAAR0NpdrC6lqCe+GC7Rg/I0ZQR+VbHAQAA6LYozQ625JOdavCHdFdxkVxc/AcAABAVpdmhdlQ36+U1ezRjVF8V5WVYHQcAAKBbozQ71OMrtyvV69aPziu0OgoAAEC3R2l2oH/urNX7W/frurEF6pXBnf8AAADMUJodJhwx9J9/26q+2Sm6+psDrI4DAABgC5Rmh3ljY6W+rGrSbRecohQvww8AANAVtCYHCYYj+u+/79Tw/ExNGpZndRwAAADboDQ7yNL1FdpT16qbvzWILeYAAACOAaXZIfyhiH77yU6d0S9b5w3qYXUcAAAAW6E0O8Sf15Vrb2NAt5zPLDMAAMCxojQ7QEswrN/9Y6fOHpirswfmWh0HAADAdijNDvDS6j2qbg7qZm5kAgAAcFwozQmu0R/SM5+V6fxTeurM/jlWxwEAALAlSnOCe+Ffu1XfGtJN5zPLDAAAcLwozQms0R/SC6t269tFvTTCl2V1HAAAANuiNCewl9fsUYM/pBvOGWh1FAAAAFujNCeolmBYf/jXbp13Sg9mmQEAAE4QpTlBvbKuXLUtQd3wb8wyAwAAnChKcwIKhCJ67p+79M2CHHbMAAAAOAkozQnotQ0VqmoM6HpmmQEAAE4KSnOCCUUM/f7TMo3sm6Wx3P0PAADgpKA0J5h3v6zSnnq/rhtbIJfLZXUcAACAhEBpTiCGYejZz3apsEeaLhjcy+o4AAAACYPSnED+WVarTXsbdc3ZA+RmlhkAAOCkoTQnkOf+uUs905M09TSf1VEAAAASCqU5QWzZ16SPt9foytH9lOJlWAEAAE4m2lWCeO6fu5TqdWvWmf2sjgIAAJBwKM0JYG+DX29t3KvLRvVRblqS1XEAAAASDqU5AfxpXbnCEUNXfaO/1VEAAAASEqXZ5oLhiP6yrlznn9pTA3LTrI4DAACQkCjNNvful/tU3RzUFWexlhkAACBWKM0299KaPSrITdU5g3pYHQUAACBhUZptbHNlo9btqdfss/pxMxMAAIAYojTb2Mtr9ijV69Ylp3MzEwAAgFiiNNtUfWtQb27aq4tH5Cs7lW3mAAAAYonSbFOvbaiUPxThAkAAAIA4oDTbkGEY+su6Co3qm6Wh+ZlWxwEAAEh4lGYbWl/eoO3Vzbp0ZB+rowAAADhCl0rz7t279fHHH0uSWltb1djYGNNQ6Nxf11coLcmtScPzrI4CAADgCKal+aWXXtJPfvITlZSUSJIqKip06623xjwYjq45ENbbm6o0cWieMpK9VscBAABwBNPS/Pzzz+uFF15QZmbb2tlBgwapuro65sFwdCu+rFJzMMzSDAAAgDgyLc3JyclKTk7ueB4KhWIaCJ1buqFSA3uk6cz+2VZHAQAAcAzTv98fM2aMFi1apNbWVn300Uf6wx/+oOLi4nhkw9dU1Ldq9a463Xx+oVzcARAAACBuTGea77zzTvXs2VNDhw7Viy++qPHjx+uOO+6IRzZ8zZsb90qSJg/PtzgJAACAs5jONLe2tmrWrFm68sorJUnhcFitra1KS0uLeTgcZBiG3ti4V2f0y9aAXL57AACAeDKdab7uuuvU2tra8by1tVXXX399TEPhSF9VNWnb/mZdPIJZZgAAgHgzLc1+v18ZGRkdzzMyMtTS0hLTUDjSmxv3yuN2adJQ9mYGAACIN9PSnJaWpg0bNnQ8X79+vVJTU2MaCocLRwy9tWmvzh3UQ7npSVbHAQAAcBzTNc3z5s3T7bffrvz8fBmGoX379uk///M/45ENB6zeVae9jQHdPp6lGQAAAFYwLc1nnHGG3njjDW3fvl2SdMoppygpidnOeHpjY6XSkzwaN7iX1VEAAAAcqUv3Yf7888+1e/duhcNhffHFF5KkGTNmxDQY2gRCEb371T5dOKSXUpM8VscBAABwJNPS/POf/1xlZWUaPny4PJ620uZyuSjNcfLJjho1+sOaxN7MAAAAljEtzevXr9frr7/OHegssuLLKmWnejV2YK7VUQAAABzLdPeMIUOGqKqqKh5Z8DWBUETvb9mv8YN7KcljOlQAAACIEdOZ5pqaGk2bNk1nnHHGYRcALlq0KKbB0LY0oykQ1sRh7M0MAABgJdPSPGfOnHjkwFG8s5mlGQAAAN2BaWkeO3ZsPHLga/yhiFZu3a8JQ3vLy9IMAAAAS5mW5jVr1uiBBx7Qtm3bFAwGFQ6HlZaWplWrVsUjn2N9Utq2NGMCt80GAACwnOkU5vz58/XYY4+psLBQa9eu1YMPPqhrrrkmHtkc7R12zQAAAOg2uvT3/oWFhQqHw/J4PJo1a5Y++OCDWOdyNH8oog+27te3i3qxNAMAAKAbMF2ekZaWpkAgoBEjRujhhx9Wfn6+IpFIPLI51qc7WJoBAADQnZhOYz788MOKRCIqKSlRenq6ysvL9fjjj8cjm2O9v3W/MpI9OruApRkAAADdgelMc//+/Tse33bbbTENAyliGPpg636dO6inkr0szQAAAOgOopbm22+/XQsXLtT06dOPenzp0qUxC+Vk68sbVN0c1PiiXlZHAQAAwAFRS/O9994riTv/xdv7W/bL43bp/FN6Wh0FAAAAB0Qtzfn5+QqHw7rnnnv07LPPxjOTo63cuk/fGJCjrFTTlTMAAACIk04XzXo8HrndbjU0NMQrj6PtqG5WaXWLxg9maQYAAEB3YjqdmZ6erunTp+u8885Tenp6x+v33XdfTIM50cqt+yVJ41jPDAAA0K2YluaLLrpIF1100XG9+cqVK/XLX/5SkUhEV1xxhX70ox8ddnzPnj26++671dDQoHA4rDvvvFPjx48/rs9KBO9v2a+heRnqm51qdRQAAAAcwrQ0X3755cf1xuFwWPPnz9fTTz8tn8+n2bNnq7i4WEVFRR3nPPXUU5oyZYq++93vasuWLfrRj36kd99997g+z+6qmwNat6deN5470OooAAAA+BrT0lxaWqrHHntMW7Zskd/v73h9xYoVnf7eunXrVFhYqIKCAknStGnTtGLFisNKs8vlUmNjoySpoaFB+fn5x/WHSAQfbquWIWn84N5WRwEAAMDXmN49Y+7cubr66qvl8Xj0zDPPaMaMGbr00ktN37iyslJ9+vTpeO7z+VRZWXnYObfddpuWLl2qcePG6Uc/+pGj10l/vL1a+ZnJGpqfYXUUAAAAfI3pTLPf79e5554rqe3ugHPmzNHMmTN1++23n/CHL1u2TJdffrluuOEGrV69WnfddZdee+01ud2Hd/nMzBR5vZ4T/rxj5fG4lZubbn7iCQqFI/p0Z60uPr2PevSgNJ+IeI0ZTg7Gy34YM/thzOyHMeueTEtzcnKyIpGICgsL9dxzz8nn86mpqcn0jX0+nyoqKjqeV1ZWyufzHXbOH//4Ry1ZskSSNHr0aPn9ftXU1KhXr8N3j2hs9MsKubnpqq1tjvnnrN1dp4bWkL7ZLysun5fI4jVmODkYL/thzOyHMbMfxsw6eXlZUY+ZLs+YN2+eWlpadN9992nDhg169dVX9dBDD5l+6KhRo1RaWqqysjIFAgEtW7ZMxcXFh53Tt29f/f3vf5ckbd26VX6/Xz17Ou9OeB+X1sjjksYO7GF1FAAAAByF6Uyzx+NRRkaGMjIytGDBgq6/sderkpIS3XjjjQqHw5o1a5aGDBmihQsXauTIkZowYYLuuece3Xffffrd734nl8ulX//613K5XCf0B7Kjv2+v1si+2dwFEAAAoJtyGYZhdHbCtddeq3379mny5MmaOnWqhg4dGq9sHaqqrLkjYTz+eqS6OaDJT32im88v1P9zTmFMP8sJ+Cste2G87Icxsx/GzH4YM+t0tjzDdGrz2WefVVVVld544w2VlJSoqalJU6ZM0Y9//OOTGtKp/rGjRpJ07iDnLUsBAACwC9M1zZKUl5en73//+/rFL36h4cOH68knn4x1Lsf4pLRGuWlJGu7LtDoKAAAAojCdad66dQTRK18AACAASURBVKtef/11LV++XLm5uZoyZYruueeeeGRLeIZh6NMdtRozMFduB67lBgAAsAvT0jxv3jxNnTpVS5YsOWLLOJyY0uoW7WsKaMzAXKujAAAAoBOmpfnFF1+MRw5H+mxn23pmSjMAAED31qU1zYiNz3bWqm92ivrnpFodBQAAAJ2gNFskHDH0r7I6jRmY68i9qQEAAOyE0myRzXsb1eAPaQx3AQQAAOj2oq5pvvnmmzv9xUWLFp30ME7y2c5aSdLZrGcGAADo9qKW5htuuEGStHz5cu3bt0+XXnqpJGnZsmXq1atXfNIlsM921ujUXunqnZFsdRQAAACYiFqax44dK0n69a9/rT//+c8drxcXF2vmzJmxT5bAAqGI1uyu14xRfayOAgAAgC4wXdPc0tKisrKyjudlZWVqaWmJaahE93l5vfyhCFvNAQAA2ITpPs1z587Vtddeq4KCAhmGoT179ugXv/hFPLIlrM921srtkr4xgNIMAABgB6aledy4cVq+fLm2bdsmSTr11FOVnMw63BPx2c5ajfBlKSvV9OsHAABAN9Cl5RlLlizRc889p+HDh2vPnj1677334pEtIbUGw9pQ0aBvFjDLDAAAYBempXnu3LlKSkrSmjVrJEk+n0+/+c1vYh4sUW2oaFA4Ymj0gGyrowAAAKCLTEvzzp079cMf/lBeb9tSgrS0NBmGEfNgiWr1rjq5JJ3Rj9IMAABgF6alOTk5Wa2trR23et65cydrmk/A2t31Gtw7Q9mpSVZHAQAAQBeZXok2Z84c3XjjjSovL9fPfvYzrV69WgsWLIhHtoQTihhat6deU0/LtzoKAAAAjoFpaT7//PN12mmnae3atTIMQ/fee6969uwZj2wJZ0tVo5qDYZ3VP8fqKAAAADgGpsszJCkQCCg7O1uZmZnaunWrPvvss1jnSkhrdtdLks7sz3pmAAAAOzGdaX7kkUf0xhtvqKioSG73wY49ZsyYmAZLRGt316lvdor6ZKdaHQUAAADHwLQ0v/POO3rzzTe5+O8EGYah1bvruXU2AACADZkuzygoKFAwGIxHloS2p75V+5sCOpOt5gAAAGzHdKY5LS1NM2bM0LnnnnvYbPN9990X02CJZv2eBknszwwAAGBHpqW5uLhYxcXF8ciS0D4vr1eq161Te2dYHQUAAADHyLQ0X3755fHIkfDWlzfotD5Z8rpdVkcBAADAMYpamm+//XYtXLhQ06dPP+rxpUuXxixUovGHItq8t1Hf/eYAq6MAAADgOEQtzffee68kadGiRXELk6i+3NuoUMTQqL5ZVkcBAADAcYhamvPz22713L9//7iFSVSfl7fd1GQkpRkAAMCWTNc0r1mzRg888IC2bdumYDCocDistLQ0rVq1Kh75EsL68gb1zU5R78wUq6MAAADgOJju0zx//nw99thjKiws1Nq1a/Xggw/qmmuuiUe2hLG+vF6n92GrOQAAALsyLc2SVFhYqHA4LI/Ho1mzZumDDz6Ida6Esa8poPJ6v0b1Y2kGAACAXXXp5iaBQEAjRozQww8/rPz8fEUikXhkSwgbyttuanJ6H0ozAACAXZnOND/88MOKRCIqKSlRenq6ysvL9fjjj8cjW0LYWNkgj0salp9pdRQAAAAcJ9OZ5kN3z7jttttiGiYRbaxs0Cm9MpSa5LE6CgAAAI5T1NIc7aYm7bi5iTnDMLSpslHnn9LT6igAAAA4AVFLMzc1OXGVDX5VNwc1gvXMAAAAtha1NB+6LKOqqkrr1q2Ty+XSqFGjlJeXF5dwdrexslGSNMLHemYAAAA7M70Q8OWXX9YVV1yht99+W2+99Za+853v6I9//GM8stnexsoGedwuFfXOsDoKAAAAToDphYBLlizRK6+8oh49ekiSampqdNVVV2n27NkxD2d3GysbNbhXOhcBAgAA2JzpTHOPHj2UkXFwpjQjI6OjQCM6wzC0saKB9cwAAAAJwHSmeeDAgbryyis1YcIEuVwurVixQsOGDdPTTz8tSbr++utjHtKOyuv9qmsNsZ4ZAAAgAXSpNA8cOLDj+YQJEyRJTU1NsUuVADZWtt0JcISPmWYAAAC7My3NP/zhD5WSknLYa9XV1erZk72HO7OxslFeLgIEAABICKZrmmfPnq01a9Z0PH/rrbd09dVXxzRUIthY0aCi3hlK9pp+xQAAAOjmTGea/+M//kPz5s3T2LFjtXfvXtXW1ur3v/99PLLZlmEY2ljZqInDelsdBQAAACeBaWkeNmyYbrnlFv385z9XRkaGnn/+efXp0yce2Wxrd12rGvwh1jMDAAAkCNPSPG/ePJWVlenVV19VaWmpbrrpJl177bW65ppr4pHPlrgTIAAAQGIxXXA7dOhQPfPMMyooKNAFF1ygl19+WRs2bIhHNtvaVNmgJI9Lg7kIEAAAICGYlubrrrtOe/bs0ccffyxJSkpK0rx582IezM6+3NukU3tlKMnDRYAAAACJwLTVvfTSS/rJT36ikpISSVJFRYVuvfXWmAezsy+rGjUkj1lmAACARGFamp9//nm98MILysxsW587aNAgVVdXxzyYXe1vCqi6OUhpBgAASCCmpTk5OVnJyckdz0OhUEwD2d1XVW0XAQ7N4yJAAACARGG6e8aYMWO0aNEitba26qOPPtIf/vAHFRcXxyObLX1V1XZ7cWaaAQAAEofpTPOdd96pnj17aujQoXrxxRc1fvx43XHHHfHIZkub9zbKl5WinLQkq6MAAADgJDGdaXa73bryyit15ZVXxiOP7X1V1cQsMwAAQIJhT7STyB+KaEd1s4ZSmgEAABIKpfkk2r6/SWFDGsJFgAAAAAmly6W5paUlljkSwpdcBAgAAJCQTEvzqlWrNHXqVE2ZMkWStGnTJt1///2xzmVLX1U1KS3JrQG5aVZHAQAAwElkWpoXLFig3/72t8rNzZUkDR8+XP/85z9jHsyOtuxru322x+2yOgoAAABOoi4tz+jbt+/hv+RmKfTR7Khu1im90q2OAQAAgJPMdMu5vn37atWqVXK5XAoGg3rmmWc0ePDgeGSzlUZ/SFWNAQ3qSWkGAABINKZTxvfff7+ef/55VVZWaty4cdq4caNKSkrikc1WdlQ3S5IG9WQ9MwAAQKIxnWmWpEcffTTWOWyvtLptd5FCZpoBAAASjmlpvvrqq9W/f39NmTJFkydPVnZ2djxy2U5pdbM8bpcG5KRaHQUAAAAnmenyjLfeekt33HGHtmzZossvv1w33XST/vrXv8Yjm62UVjerIDdVXg8XSQIAACSaLjW8M844Q3PnztXLL7+snJwc3XPPPbHOZTs7qltU2IOlGQAAAInIdHlGY2Oj3n77bS1btkxlZWWaOHGiXn755Xhks41wxNCuuhadf2pPq6MAAAAgBkxL86WXXqqJEyfq1ltv1ejRo+ORyXYqG/wKhg0V9GDnDAAAgERkWppXrFghl4s73HWmrKZt54yB3D4bAAAgIUUtzb/85S9177336pZbbjnq8UWLFsUslN2U1baVZmaaAQAAElPU0nzZZZdJkm644Ya4hbGrstoWpXjdystMtjoKAAAAYiBqaR45cqQkaePGjfrBD35w2LHf//73Gjt2bGyT2cjOmhYV5KbJzTIWAACAhGS65dxf/vKXI1575ZVXYhLGrspqWliaAQAAkMCizjS/9tpreu2117Rr1y7dfPPNHa83NTUpJycnLuHsIBQxtLuuVeOLelsdBQAAADEStTSPHj1aeXl5qqmpOWxdc0ZGhoYNGxaXcHZQUd+qUMTQwB7cPhsAACBRRS3N/fv3V//+/fXiiy/GM4/tsHMGAABA4jPdp3nNmjV64IEHtG3bNgWDQYXDYaWlpWnVqlXxyNftsUczAABA4jO9EHD+/Pl67LHHVFhYqLVr1+rBBx/UNddcE49strCzpkXpSR71ymC7OQAAgERlWpolqbCwUOFwWB6PR7NmzdIHH3zQpTdfuXKlJk+erEmTJmnx4sVHHP/Vr36lyy67TJdddpkmT56ss88++9jSdwNltS0akJvKXRMBAAASmOnyjLS0NAUCAY0YMUIPP/yw8vPzFYlETN84HA5r/vz5evrpp+Xz+TR79mwVFxerqKio45x58+Z1PH722Wf1xRdfHOcfwzplNS0alp9pdQwAAADEkOlM88MPP6xIJKKSkhKlp6ervLxcjz/+uOkbr1u3ToWFhSooKFBycrKmTZumFStWRD1/2bJluuSSS44tvcVCEUN76v3qz3pmAACAhGY609y/f/+Ox7fddluX37iyslJ9+vTpeO7z+bRu3bqjnrt7927t2rVL55xzTpffvzvY1+hXOGKoXw7bzQEAACSyqKV5+vTpnf7i0qVLT1qIZcuWafLkyfJ4PEc9npmZIq/36MdiyeNxKzc3PerxL2tbJUlD+uV0eh7ix2zM0L0wXvbDmNkPY2Y/jFn3FLU0L1q06ITe2OfzqaKiouN5ZWWlfD7fUc99/fXXVVJSEvW9Ghv9J5TleOXmpqu2tjnq8S176iRJ2W51eh7ix2zM0L0wXvbDmNkPY2Y/jJl18vKyoh7r9OYmJ2LUqFEqLS1VWVmZfD6fli1bpkcfffSI87Zu3ar6+nqNHj36hD7PCnvq2maa+2SzPAMAACCRma5pHj16dMd2asFgUKFQqEs3N/F6vSopKdGNN96ocDisWbNmaciQIVq4cKFGjhypCRMmSGqbZZ46daott2wrr29V74xkpXi7tHMfAAAAbMq0NK9evbrjsWEYWrFihdasWdOlNx8/frzGjx9/2Gu33377Yc/nzJnTpffqjvbU+9WXWWYAAICEd0xTpC6XSxMnTtSHH34Yqzy2Ul7Xqr7ZKVbHAAAAQIyZzjQvX76843EkEtH69euVkkJRDEcMVTT4NXFYntVRAAAAEGOmpfm9997reOzxeNS/f389+eSTMQ1lB1XtezQz0wwAAJDwTEvzggUL4pHDdsrr27bB68uNTQAAABKeaWkuKyvTc889p927dysUCnW8fqL7ONtdeX3bdnNcCAgAAJD4TEvzrbfeqtmzZ+vCCy+U283Wau3aS3OfLJZnAAAAJDrT0pySkqLvf//78chiK+V1fvXKSFZqUvxv7w0AAID4Mi3N3//+9/XEE0/o/PPPV3Jycsfrp59+ekyDdXd76lu5CBAAAMAhTEvzl19+qb/+9a/65JNPOu7a53K59Mwzz8Q8XHdWXt+qEb7o9ycHAABA4jAtzW+++abeeeedw2aZnS4cMVRR71fxEPZoBgAAcALTK/uGDBmihoaGeGSxjf1NAYUiBncDBAAAcAjTmeaGhgZNmTJFo0aNUlJSUsfrTt5yrqqxbY/mfHbOAAAAcATT0jxnzpx45LCVqsaAJCkvkyUrAAAATmBamseOHRuPHLayr6mtNPfOoDQDAAA4gWlpHj16dMeuGcFgUKFQSGlpaVq1alXMw3VX+5oCcknqkU5pBgAAcALT0rx69eqOx4ZhaMWKFVqzZk1MQ3V3+xoD6pmRLK/bZXUUAAAAxMEx3Rfb5XJp4sSJ+vDDD2OVxxb2NQVYmgEAAOAgpjPNy5cv73gciUS0fv16paQ4e9cISjMAAICzmJbm9957r+Oxx+NR//799eSTT8Y0VHdX1ejXcF+m1TEAAAAQJ6alecGCBfHIYRuhiKGa5iAzzQAAAA5iuqb57rvvVn19fcfzuro6zZ07N6ahurPqpoAMsd0cAACAk5iW5s2bNys7O7vjeU5OjjZu3BjTUN0ZezQDAAA4j2lpjkQiqqur63heW1urcDgc01DdWU1zUJLUi9IMAADgGKZrmm+44QZ95zvf0cUXXyxJevPNN3XzzTfHPFh3Vd3cNtPcIz3J4iQAAACIF9PSPGPGDI0cOVKffPKJJOmJJ55QUVFRzIN1V+0zzZRmAAAA5zAtzZJUVFTk6KJ8qJqWoFK8bqUneayOAgAAgDg5pjsCQqppDqhHWpJcLm6hDQAA4BSU5mNU3RxkaQYAAIDDUJqPUU1zUD3T2TkDAADASSjNx6imJahcZpoBAAAchdJ8DAzDUE1zQD3TKM0AAABOQmk+Bk2BsAJhgzXNAAAADkNpPgbtezSzphkAAMBZKM3HoKaFG5sAAAA4EaX5GNQcuIV2LmuaAQAAHIXSfAzqWkKSKM0AAABOQ2k+BnWtbcszctK6dPdxAAAAJAhK8zGobQnJ63YpPcljdRQAAADEEaX5GNS1BpWTliSXy2V1FAAAAMQRpfkY1LUElZPK0gwAAACnoTQfg7rWEKUZAADAgSjNx6CupW15BgAAAJyF0nwM2maaKc0AAABOQ2nuIsMwDsw0szwDAADAaSjNXdQcDCsUMZhpBgAAcCBKcxe13w2QmWYAAADnoTR3UX373QCZaQYAAHAcSnMXHZxppjQDAAA4DaW5i+oOzDRns08zAACA41Cau6iu9cBMM6UZAADAcSjNXdTobyvNWaxpBgAAcBxKcxfVt4aU4nUrxctXBgAA4DQ0wC5q8IeUmcLSDAAAACeiNHdRoz+kbEozAACAI1Gau6ihlZlmAAAAp6I0d1GDP6SsVI/VMQAAAGABSnMXNfhDymKmGQAAwJEozV3U0EppBgAAcCpKcxcYhqFGf0hZ3NgEAADAkSjNXdAcDCtsiJlmAAAAh6I0d0HDgVtoU5oBAACcidLcBQ0dt9CmNAMAADgRpbkLOkozM80AAACORGnugobWsCRmmgEAAJyK0twFjcw0AwAAOBqluQvqKc0AAACORmnugvaZ5gxKMwAAgCNRmrugJRBWitctr9tldRQAAABYgNLcBc3BsNKTPFbHAAAAgEUozV3QEgwrLZnSDAAA4FSU5i5oDjDTDAAA4GSU5i5oCYaVRmkGAABwLEpzFzQHIkpP5qsCAABwKppgFzDTDAAA4GyU5i5opjQDAAA4GqW5C1oCYaWzewYAAIBjUZq7gJlmAAAAZ6M0mwhHDPlDEbacAwAAcDBKs4mWYFiSuLkJAACAg1GaTbSX5vQkvioAAACnogmaaAlGJDHTDAAA4GSUZhMtgfaZZkozAACAU8W0NK9cuVKTJ0/WpEmTtHjx4qOe8/rrr2vq1KmaNm2afvazn8UyznFpbl/TTGkGAABwLG+s3jgcDmv+/Pl6+umn5fP5NHv2bBUXF6uoqKjjnNLSUi1evFgvvPCCcnJytH///ljFOW7tpZl9mgEAAJwrZjPN69atU2FhoQoKCpScnKxp06ZpxYoVh53z0ksv6ZprrlFOTo4kqVevXrGKc9zal2cw0wwAAOBcMZtprqysVJ8+fTqe+3w+rVu37rBzSktLJUlXXXWVIpGIbrvtNo0bN+6I98rMTJHXG//S6vG4pQNluU/vTOXmpsU9A46Nx+NWbm661THQRYyX/TBm9sOY2Q9j1j3FrDR3RTgc1o4dO/Tss8+qoqJC3/ve97R06VJlZ2cfdl5jo9+SfLm56dpf2yJJCrYEVCvDkhzoutzcdNXWNlsdA13EeNkPY2Y/jJn9MGbWycvLinosZsszfD6fKioqOp5XVlbK5/MdcU5xcbGSkpJUUFCgQYMGdcw+dxcda5pZngEAAOBYMSvNo0aNUmlpqcrKyhQIBLRs2TIVFxcfds7EiRP16aefSpKqq6tVWlqqgoKCWEU6Li3BsDxul5I8LqujAAAAwCIxW57h9XpVUlKiG2+8UeFwWLNmzdKQIUO0cOFCjRw5UhMmTNAFF1ygjz76SFOnTpXH49Fdd92lHj16xCrScWkOhJWe5JHLRWkGAABwKpdhGN1+oW5VVYMln5ubm66fvbhan5TWaNlN51iSAceGdWD2wnjZD2NmP4yZ/TBm1rFkTXOiaA5E2KMZAADA4SjNJlqCYfZoBgAAcDhKs4nmYJiZZgAAAIejNJtoCTDTDAAA4HSUZhPNwTB7NAMAADgcpdlESzCsNJZnAAAAOBql2UT7Ps0AAABwLkpzJwzDYKYZAAAAlObO+EMRRQwx0wwAAOBwlOZONAXCksTuGQAAAA5Hae5EcyAkSUpP5msCAABwMtpgJ5r9bTPNLM8AAABwNkpzJ9pnmrkQEAAAwNkozZ1oX9PMTDMAAICzUZo70cyFgAAAABCluVMHLwSkNAMAADgZpbkTbDkHAAAAidLcqfblGcw0AwAAOBuluRPN/pBcklK8fE0AAABORhvsRHMgrLQkj9wul9VRAAAAYCFKcyeaAiGlJvEVAQAAOB2NsBPtM80AAABwNkpzJwKhiJJZzwwAAOB4NMJOhCKGvG7WMwMAADgdpbkToXCE0gwAAABKc2eCEUNeN18RAACA09EIO9E202x1CgAAAFiNStiJUMSQx8NXBAAA4HQ0wk5wISAAAAAkSnOnQmFKMwAAACjNnWL3DAAAAEiU5k6F2D0DAAAAojR3KhiOyOthphkAAMDpKM2d4EJAAAAASJTmTnEhIAAAACRKc6dCES4EBAAAAKW5U8GwIS83NwEAAHA8GmEnmGkGAACARGnuFGuaAQAAIFGaozIMg90zAAAAIInSHFU4YkgS+zQDAACA0hxN6EBp9rgozQAAAE5HaY4i1DHTzFcEAADgdDTCKELhA6WZNc0AAACOR2mOImRQmgEAANCG0hxFKByRRGkGAAAApTmqELtnAAAA4ABKcxQdpdnNVwQAAOB0NMIoDpZmZpoBAACcjtIcRZjdMwAAAHAApTmKUOTAhYCsaQYAAHA8SnMULM8AAABAO0pzFFwICAAAgHY0wii4IyAAAADaUZqjYJ9mAAAAtKM0R9FxISAzzQAAAI5HaY6CNc0AAABoRyOMon1Ns4eZZgAAAMejNEfBlnMAAABoR2mOIsyFgAAAADiA0hwFFwICAACgHaU5CpZnAAAAoB2lOQp2zwAAAEA7GmEUHXcEZE0zAACA41Gao2B5BgAAANpRmqPgQkAAAAC0ozRHEYoY8rhdcrkozQAAAE5HaY4iFDaYZQYAAIAkSnNUoYjBRYAAAACQRGmOKhQxlMR2cwAAABClOapQJMJMMwAAACRRmqNqW9PM1wMAAABKc1SsaQYAAEA7SnMUoQi7ZwAAAKANpTmKcITlGQAAAGhDK4yC5RkAAABoR2mOIhSJKInSDAAAAFGao2L3DAAAALTzWh2gu5owtLe8KUlWxwAAAEA3QGmOYuaZ/ZSbm67a2marowAAAMBirD8AAAAATMS0NK9cuVKTJ0/WpEmTtHjx4iOO//nPf9Y555yjyy67TJdddplefvnlWMYBAAAAjkvMlmeEw2HNnz9fTz/9tHw+n2bPnq3i4mIVFRUddt7UqVNVUlISqxgAAADACYvZTPO6detUWFiogoICJScna9q0aVqxYkWsPg4AAACImZiV5srKSvXp06fjuc/nU2Vl5RHnLV++XNOnT9dPfvITlZeXxyoOAAAAcNws3T3jwgsv1CWXXKLk5GT97//+r+6++24988wzR5yXmZkir9cT93wej1u5uelx/1wcP8bMXhgv+2HM7Icxsx/GrHuKWWn2+XyqqKjoeF5ZWSmfz3fYOT169Oh4fMUVV+iRRx456ns1NvpjE9IEW87ZD2NmL4yX/TBm9sOY2Q9jZp28vKyox2K2PGPUqFEqLS1VWVmZAoGAli1bpuLi4sPO2bt3b8fjd999V4MHD45VHAAAAOC4xWym2ev1qqSkRDfeeKPC4bBmzZqlIUOGaOHChRo5cqQmTJigZ599Vu+++648Ho9ycnK0YMGCWMUBAAAAjpvLMAzD6hBmqqoaLPlc/nrEfhgze2G87Icxsx/GzH4YM+tYsjwDAAAASBSUZgAAAMAEpRkAAAAwQWkGAAAATFCaAQAAABOUZgAAAMAEpRkAAAAwQWkGAAAATFCaAQAAABOUZgAAAMCELW6jDQAAAFiJmWYAAADABKUZAAAAMEFpBgAAAExQmqNYuXKlJk+erEmTJmnx4sVWx4GkuXPn6txzz9Ull1zS8Vptba2uv/56XXTRRbr++utVV1cnSTIMQw8++KAmTZqk6dOna8OGDVbFdrTy8nJde+21mjp1qqZNm6bf//73khi37srv92v27Nm69NJLNW3aNP3Xf/2XJKmsrExXXHGFJk2apDvuuEOBQECSFAgEdMcdd2jSpEm64oortGvXLivjO1o4HNaMGTN00003SWLMurvi4mJNnz5dl112mWbOnCmJ/120A0rzUYTDYc2fP19LlizRsmXL9Nprr2nLli1Wx3K8mTNnasmSJYe9tnjxYv3/7d17TNX1G8Dx94Hj8QoimscK18BFM4FKRYQ0Bh4vBRpeyyUKuv4wL3kZzelWJpPGhtZssdmmThOdFggtNS9gYl4QLcO8tJyTjii4HU4eDyRyDs/vD+u7+Hk59bM8+ON5/XW+9+fhYZ/z8OXzPSc+Pp69e/cSHx9v/IFTXl7OpUuX2Lt3L9nZ2SxfvtwPEavAwECWLFnCrl272LZtG1u2bOHChQtatzbKYrGwceNGvvzyS4qLizl06BCnTp0iLy+PjIwM9u3bR3BwMF988QUAn3/+OcHBwezbt4+MjAzy8vL8nEH7tWnTJvr162csa83avo0bN1JSUkJRURGg72ePAm2a76KqqoqnnnqKvn37YrFYSElJobS01N9htXuxsbF079691brS0lLS0tIASEtLY//+/a3Wm0wmnn/+eVwuF9euXXvoMbd3vXv3ZsCAAQB069aNiIgI6urqtG5tlMlkomvXrgB4PB48Hg8mk4ljx44xevRoAMaPH2+Mh2VlZYwfPx6A0aNHc/ToUfQDmR6+2tpavvnmGyZNmgTcvjOpNXv06LjY9mnTfBd1dXX06dPHWLZardTV1fkxInUvDoeD3r17A/DYY4/hcDiAO2vYp08fraGfXb58mXPnzvHcc89p3dowr9fLq6++SkJCAgkJCfTt25fg4GDMZjPQuiZ1dXU8/vjjAJjNZoKCgnA6nX6Lvb3Kyckhyly9bQAACZVJREFUKyuLgIDbb+lOp1Nr9giYNWsWEyZMYNu2bYC+nz0KzP4OQKl/islkwmQy+TsMdRcNDQ3Mnz+fpUuX0q1bt1bbtG5tS2BgICUlJbhcLubMmcPFixf9HZK6jwMHDhAaGkpUVBQVFRX+Dkf9RVu3bsVqteJwOMjMzCQiIqLVdh0X2yZtmu/CarVSW1trLNfV1WG1Wv0YkbqXnj17cu3aNXr37s21a9cIDQ0F7qxhbW2t1tBPmpubmT9/PmPHjmXUqFGA1u1REBwcTFxcHKdOncLlcuHxeDCbza1qYrVauXr1Kn369MHj8XDjxg169Ojh58jbl++++46ysjLKy8tpamrC7XazcuVKrVkb90c9evbsyciRI6mqqtJx8RGg0zPuIjo6mkuXLmG327l16xY7d+4kOTnZ32Gpu0hOTqa4uBiA4uJiRowY0Wq9iHDq1CmCgoKMf3uph0dEWLZsGREREWRmZhrrtW5tU319PS6XC4CbN29y5MgR+vXrR1xcHHv27AFgx44dxniYnJzMjh07ANizZw9Dhw7Vu2MP2eLFiykvL6esrIzVq1czdOhQVq1apTVrwxobG3G73cbrw4cP8/TTT+u4+AjQr9G+h4MHD5KTk4PX62XixInMnj3b3yG1e4sWLeL48eM4nU569uzJvHnzsNlsLFiwgKtXr/LEE0/w0UcfERISgoiwYsUKDh06ROfOncnJySE6OtrfKbQ7J06c4I033iAyMtKYb7lo0SJiYmK0bm3Q+fPnWbJkCV6vFxFhzJgxzJ07F7vdzsKFC7l+/Tr9+/cnLy8Pi8VCU1MTWVlZnDt3ju7du/Phhx/St29ff6fRblVUVLB+/XrWrl2rNWvD7HY7c+bMAW4/Q5Camsrs2bNxOp06LrZx2jQrpZRSSinlg07PUEoppZRSygdtmpVSSimllPJBm2allFJKKaV80KZZKaWUUkopH7RpVkoppZRSygdtmpVS6h+Snp7O6dOn//XrbNq0iZdffpnFixf/69fyJ5fLRUFBgb/DUEopQJtmpZRqEzwez1/ed8uWLWzYsIFVq1b9ixH5n8vlYuvWrf4OQymlAP2cZqVUO3P58mXefPNNBg0axPfff4/VaiU/P59OnTqRnp7OO++8Q3R0NPX19UyaNImysjKKiorYv38/v/32G9XV1cycOZPm5mZKSkqwWCx8+umnhISEkJ6ezjPPPENlZSVer5ecnBxiYmJobGwkOzubn3/+GY/Hw9y5c7HZbBQVFbF3714aGxtpaWlh8+bNrWLdsGEDhYWFAEyaNImMjAzeffddioqKCA8PZ+LEiWRkZBj7e71e8vLyOHToECaTiSlTppCens7Ro0fJzc3F6/USFRXF+++/j8ViITk5mZSUFMrLywkMDCQ7O5vVq1dTXV3NrFmzmDp1KhUVFaxZs4auXbtSXV1NXFwcy5cvJyAggK+++oq1a9ciIiQmJpKVlQXACy+8wPTp0zlw4ACdOnUiPz+fXr16UV9fz3vvvceVK1cAWLp0KYMGDeLjjz/mypUrXL58mStXrjBjxgymT5/OwoULKS0tJTw8nISEBDIzM1m4cCFutxuv18vy5csZPHjww/nFUUopUUqpdsRut0v//v3l7NmzIiIyf/58KS4uFhGRadOmSVVVlYiIOBwOSUpKEhGRwsJCsdlscuPGDXE4HDJw4EDZsmWLiIisXLlSNmzYYBy/bNkyERE5fvy4pKSkiIjIqlWrjGtcv35dRo0aJQ0NDVJYWCjDhw8Xp9N5R5ynT5+W1NRUaWhoELfbLa+88oqcOXNGRESSkpLE4XDccUxBQYHMmzdPmpubRUTE6XTKzZs35aWXXpKLFy+KiEhWVpYRb1JSkhQUFBh5pKamGjnGx8eLiMixY8ckKipKfvnlF/F4PJKRkSG7d++W2tpaSUxMFIfDIc3NzZKeni779u0TEZHIyEgpLS0VEZHc3Fz55JNPRERk0aJFUllZKSIiNTU1MmbMGBERWbNmjbz22mvS1NQkDodDhgwZIrdu3RK73W78DEVE1q1bJ/n5+SIi4vF45MaNG/crtVJK/aPM/m7alVLqYQsLC6N///4ADBgwgJqaGp/HxMXF0a1bNwCCgoJITk4GIDIykp9++snYLyUlBYDY2Fjcbjcul4tvv/2WsrIy1q9fD0BTUxNXr14F4MUXXyQkJOSO6508eRKbzUaXLl0AGDlyJCdOnODZZ5+9Z4xHjx7l9ddfx2y+PbSHhIRw/vx5wsLCCA8PB2D8+PEUFBQYd6hHjBhh5NHY2GjkaLFYcLlcAMTExBhftZySksLJkycxm80MGTKE0NBQAMaOHUtlZSU2m40OHTqQlJQEQFRUFIcPHwbgyJEjXLhwwYjX7XbT0NAAQGJiIhaLhdDQUEJDQ3E4HHfkFx0dzdKlS/F4PNhsNqOGSin1MGjTrJRqdywWi/E6MDCQpqYm47X8PmPt1q1b9zwmICCADh06GK+9Xq+xzWQytTruj+U1a9YQERHRatsPP/xA586dHzSdB/LnPP47xz/mWd8rp/ud8499/vzzaWlpYfv27XTs2PGOY/67Jneb4x0bG8vmzZs5ePAgS5YsITMzk7S0tL+SplJKPTB9EFAppX735JNP8uOPPwLw9ddf/0/n2LVrFwAnTpwgKCiIoKAghg0bxubNm42G/OzZsz7PM3jwYGMedWNjI/v37/c5fzchIYFt27YZDeevv/5KeHg4NTU1VFdXA1BSUkJsbOzfyqmqqgq73U5LSwu7d+9m0KBBxMTEUFlZSX19PV6vl507d/o877Bhw/jss8+M5XPnzt13/65duxp3ogFqamro1asXU6ZMYfLkyZw5c+Zv5aGUUg9C7zQrpdTvZs6cyYIFC9i+fTuJiYn/0zk6duxIWloaHo+HnJwcAN566y1ycnIYN24cLS0thIWFsXbt2vueZ8CAAUyYMIHJkycDtx8EvN/UDIDJkydz6dIlxo0bh9lsZsqUKUybNo0PPviAt99+23gQcOrUqX8rp+joaLKzs40HAUeOHElAQACLFy9mxowZxoOANpvtvudZtmwZK1asYOzYsXi9XgYPHsyKFSvuuX+PHj0YOHAgqampDB8+nMjISNatW4fZbKZLly7k5ub+rTyUUupB6KdnKKWUuqeKigrWr1/vs8lXSqn/dzo9QymllFJKKR/0TrNSSimllFI+6J1mpZRSSimlfNCmWSmllFJKKR+0aVZKKaWUUsoHbZqVUkoppZTyQZtmpZRSSimlfNCmWSmllFJKKR/+A4w+dlAfLDPTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "f0664d287ec08bb891d3c12fc87977bf92c97ae3",
        "id": "CwpmoYEpqiUo",
        "colab_type": "text"
      },
      "source": [
        "**Question 1:** <br>\n",
        "What is the minimum number of principal components required to cover the 90% of the variance of the original (scaled) data?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "c67c5a22dec7dbc1bfa362d85ddad72334cb6ffb",
        "id": "aD82RDgCqiUp",
        "colab_type": "code",
        "outputId": "e3704510-90c2-4f06-f262-ee202561a432",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        }
      },
      "source": [
        "pca=PCA(n_components=66,random_state=17)\n",
        "xpc=pca.fit_transform(xstd)\n",
        "xpc"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-16.38097982,   1.99508303,   3.41568386, ...,  -0.67756537,\n",
              "         -2.06697894,  -0.28968171],\n",
              "       [-15.58217328,   1.18259362,  -0.32120465, ...,  -0.91411373,\n",
              "         -0.46793756,  -0.1016326 ],\n",
              "       [-15.42398918,   2.24316639,  -1.23778699, ...,  -1.02037135,\n",
              "         -0.47378491,   0.38232422],\n",
              "       ...,\n",
              "       [ 11.84685312,  -3.43356522,   2.18801501, ...,  -1.08117468,\n",
              "          1.33233843,  -2.12200408],\n",
              "       [  8.20668637,  -4.38648805,   1.31871699, ...,  -1.05078445,\n",
              "          0.90713141,   1.06355701],\n",
              "       [  9.31117251,  -3.98616101,   1.93301456, ...,  -1.31180942,\n",
              "         -1.33168191,  -0.33616371]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-9ROFCA3ro_",
        "colab_type": "code",
        "outputId": "f7d90e18-0013-4b76-894b-d6c7201d1c55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "s=pca.explained_variance_ratio_\n",
        "print(s)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.50738221 0.06239186 0.02692564 0.02452871 0.01888936 0.01631395\n",
            " 0.01414533 0.01216211 0.00985248 0.00949228 0.00858304 0.0080998\n",
            " 0.00765862 0.00676667 0.0063024  0.00615717 0.00595334 0.00577193\n",
            " 0.0056343  0.00540827 0.00518823 0.00502739 0.0047322  0.00463807\n",
            " 0.00446834 0.00439856 0.00416849 0.00400753 0.00389007 0.00381285\n",
            " 0.00366818 0.00364243 0.00349103 0.00346642 0.00335491 0.00329351\n",
            " 0.00323766 0.00300372 0.00293956 0.00290223 0.00275389 0.0026924\n",
            " 0.00263588 0.00257685 0.00254883 0.00246243 0.00244097 0.00237034\n",
            " 0.0023478  0.00230484 0.00226826 0.00217151 0.0021133  0.00205235\n",
            " 0.00200965 0.00200008 0.00193958 0.00191819 0.00190165 0.00187247\n",
            " 0.00180231 0.00178205 0.00175374 0.00171551 0.00167247 0.00164747]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zic137744dkm",
        "colab_type": "code",
        "outputId": "16a13b58-7fff-4762-ea50-383abc472220",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "s.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(66,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "a3d8270e6546cb8b38b7cd47909eff93d77716bc",
        "id": "sO_Go03CqiUs",
        "colab_type": "text"
      },
      "source": [
        "**Answer options:**\n",
        "- 56 \n",
        "- 65\n",
        "- 66\n",
        "- 193"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "3fcec6dd81af6f09364cb011ba0994d4e5852f43",
        "id": "JYBKmf76qiUt",
        "colab_type": "code",
        "outputId": "925b81c2-bec0-4de6-8f35-aff43f01076f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "round(s[0],2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.51"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "baf5451408baf763d178b01a5d75e37105f4446e",
        "id": "0CdWbF8fqiUs",
        "colab_type": "text"
      },
      "source": [
        "**Question 2:**<br>\n",
        "What percentage of the variance is covered by the first principal component? Round to the nearest percent.\n",
        "\n",
        "**Answer options:**\n",
        "- 45\n",
        "- 51 \n",
        "- 56\n",
        "- 61"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "53787132590f0f8b8f9b44d47981f9de969b89f2",
        "id": "bzn8PSt7qiUv",
        "colab_type": "text"
      },
      "source": [
        "Visualize data in projection on the first two principal components."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zT4K_N87Erp",
        "colab_type": "code",
        "outputId": "12d19cd4-cfc6-47e2-d8aa-cdc42eb87558",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        }
      },
      "source": [
        "plt.scatter(xpc[:,0],xpc[:,1] , c=y, s=20, cmap='viridis');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "e54a8eb905c5d9b6180083fab0ef8f74ca582c62",
        "id": "83nZMvdJqiUy",
        "colab_type": "text"
      },
      "source": [
        "**Question 3:**<br>\n",
        "If everything worked out correctly, you will see a number of clusters, almost perfectly separated from each other. What types of activity are included in these clusters? <br>\n",
        "\n",
        "**Answer options:**\n",
        "- 1 cluster: all 6 activities\n",
        "- 2 clusters: (walking, walking upstairs, walking downstairs ) and (sitting, standing, laying)\n",
        "- 3 clusters: (walking), (walking upstairs, walking downstairs) and (sitting, standing, laying)\n",
        "- 6 clusters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "8880c32278abd73e16764a6b842717cd783b66cf",
        "id": "rXqy3G-aqiUz",
        "colab_type": "text"
      },
      "source": [
        "------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "37b0cfd306d225c21ae1e2a8abb882870a24271b",
        "id": "k_3MYQ3uqiUz",
        "colab_type": "text"
      },
      "source": [
        "Perform clustering with the `KMeans` method, training the model on data with reduced dimensionality (by PCA). In this case, we will give a clue to look for exactly 6 clusters, but in general case we will not know how many clusters we should be looking for.\n",
        "\n",
        "Options:\n",
        "\n",
        "- ** n_clusters ** = n_classes (number of unique labels of the target class)\n",
        "- ** n_init ** = 100\n",
        "- ** random_state ** = RANDOM_STATE (for reproducibility of the result)\n",
        "\n",
        "Other parameters should have default values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "0d6d17a62f335bb062ea0e5134cb03ff60cb085b",
        "id": "pZ4uciWVqiU0",
        "colab_type": "code",
        "outputId": "2e3359a5-0b9f-4d8e-b557-3e4308132c0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "km=KMeans(n_clusters=n_classes,n_init=100,max_iter=300,random_state=17)\n",
        "y_kmeans=km.fit_predict(xpc)\n",
        "y_kmeans\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 5, 5, ..., 3, 3, 3], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "e34d43b9b3ebd34ad01c3a1d9d1a4dde85a4e832",
        "id": "58b1vORfqiU2",
        "colab_type": "text"
      },
      "source": [
        "Visualize data in projection on the first two principal components. Color the dots according to the clusters obtained."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "c05aa57fd57870aaa25543be822a75801a80b9a2",
        "id": "UJnMg_vAqiU3",
        "colab_type": "code",
        "outputId": "60bd8991-8b38-4881-b609-c7ae707ce775",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        }
      },
      "source": [
        "# Your code here\n",
        "plt.scatter(xpc[:,0],xpc[:,1],c=y_kmeans, s=20, cmap='viridis');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "0d84b356e4e6f91f05fdb9a526ccfdb099523882",
        "id": "Svpg4sbNqiU5",
        "colab_type": "text"
      },
      "source": [
        "Look at the correspondence between the cluster marks and the original class labels and what kinds of activities the `KMeans` algorithm is confused at."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "617e35a6ce5f78fa73d315b5cdbcad8e29c8cf21",
        "id": "49VmPH4UqiU6",
        "colab_type": "code",
        "outputId": "4c07cd4f-8068-4db7-eeff-6ec1cd348c27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        }
      },
      "source": [
        " tab = pd.crosstab(y,y_kmeans, margins=True)\n",
        " tab.index = ['walking', 'going up the stairs',\n",
        "             'going down the stairs', 'sitting', 'standing', 'laying', 'all']\n",
        " tab.columns = ['cluster' + str(i + 1) for i in range(6)] + ['all']\n",
        "tab"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>cluster1</th>\n",
              "      <th>cluster2</th>\n",
              "      <th>cluster3</th>\n",
              "      <th>cluster4</th>\n",
              "      <th>cluster5</th>\n",
              "      <th>cluster6</th>\n",
              "      <th>all</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>walking</th>\n",
              "      <td>740</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>904</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>going up the stairs</th>\n",
              "      <td>296</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1241</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>going down the stairs</th>\n",
              "      <td>890</td>\n",
              "      <td>0</td>\n",
              "      <td>196</td>\n",
              "      <td>320</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sitting</th>\n",
              "      <td>0</td>\n",
              "      <td>451</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>91</td>\n",
              "      <td>1234</td>\n",
              "      <td>1777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>standing</th>\n",
              "      <td>0</td>\n",
              "      <td>565</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1341</td>\n",
              "      <td>1906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>laying</th>\n",
              "      <td>0</td>\n",
              "      <td>330</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1556</td>\n",
              "      <td>53</td>\n",
              "      <td>1944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>all</th>\n",
              "      <td>1926</td>\n",
              "      <td>1348</td>\n",
              "      <td>279</td>\n",
              "      <td>2471</td>\n",
              "      <td>1647</td>\n",
              "      <td>2628</td>\n",
              "      <td>10299</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       cluster1  cluster2  cluster3  ...  cluster5  cluster6    all\n",
              "walking                     740         0        78  ...         0         0   1722\n",
              "going up the stairs         296         2         5  ...         0         0   1544\n",
              "going down the stairs       890         0       196  ...         0         0   1406\n",
              "sitting                       0       451         0  ...        91      1234   1777\n",
              "standing                      0       565         0  ...         0      1341   1906\n",
              "laying                        0       330         0  ...      1556        53   1944\n",
              "all                        1926      1348       279  ...      1647      2628  10299\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "047f89c9981e696776ee98f87d0934ee1b08ed98",
        "id": "bMlK7z9oqiU_",
        "colab_type": "text"
      },
      "source": [
        "We see that for each class (i.e., each activity) there are several clusters. Let's look at the maximum percentage of objects in a class that are assigned to a single cluster. This will be a simple metric that characterizes how easily the class is separated from others when clustering.\n",
        "\n",
        "Example: if for class \"walking downstairs\" (with 1406 instances belonging to it), the distribution of clusters is:\n",
        " - cluster 1 - 900\n",
        " - cluster 3 - 500\n",
        " - cluster 6 - 6,\n",
        " \n",
        "then such a share will be 900/1406 $ \\approx $ 0.64.\n",
        " \n",
        "\n",
        "**Question 4:** <br>\n",
        "Which activity is separated from the rest better than others based on the simple metric described above? <br>\n",
        "\n",
        "**Answer:**\n",
        "- walking\n",
        "- standing\n",
        "- walking downstairs\n",
        "- all three options are incorrect"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "c51f76505725d754a6d54f7bd01dedff7d5dc288",
        "id": "Kn-uPOdrqiU_",
        "colab_type": "text"
      },
      "source": [
        "It can be seen that kMeans does not distinguish activities very well. Use the elbow method to select the optimal number of clusters. Parameters of the algorithm and the data we use are the same as before, we change only `n_clusters`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "90c60a1f8bce02bfedd8233190b7ff81fb134d35",
        "id": "XEEmASOIqiVA",
        "colab_type": "code",
        "outputId": "40c69be2-a33d-48a0-8fe4-ade2f61558d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 685,
          "referenced_widgets": [
            "86ae2975cf7342dfbac5525a5174c7f7",
            "1484df3b59284d84b1764c86ffeb4037",
            "85b4aaaaf8ec49938c734fc5ab8cac79",
            "757a4636583642b4b917d64f504bef49",
            "68165592076349d49afe2e36c2da667a",
            "b14cbd72d6eb4db2ad5148f6336a3fbc",
            "b0b84c382ef9494aaf9663177b1b824e",
            "0012dc91f08c4b32b372cbdbc49e4a1e"
          ]
        }
      },
      "source": [
        "# # Your code here\n",
        " inertia = []\n",
        " for k in tqdm_notebook(range(1, n_classes + 1)):\n",
        "     kmeans = KMeans(n_clusters = k, init = 'k-means++', max_iter = 300, n_init = 100, random_state = 17)\n",
        "     kmeans.fit(xpc)\n",
        "     inertia.append(kmeans.score(xpc))\n",
        "plt.plot(range(1,n_classes + 1),inertia)\n",
        "plt.title('The elbow method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('inertia')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "86ae2975cf7342dfbac5525a5174c7f7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "5f9d80ac0bea5173134d880a5c1faf233456cd12",
        "id": "r2BZkOyCqiVD",
        "colab_type": "text"
      },
      "source": [
        "**Question 5:** <br>\n",
        "How many clusters can we choose according to the elbow method? <br>\n",
        "\n",
        "**Answer options:**\n",
        "- 1\n",
        "- 2\n",
        "- 3\n",
        "- 4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "f4b2c7c0bf71844e2aa1981b20bee09d9064255d",
        "id": "BMzw4HfIqiVD",
        "colab_type": "text"
      },
      "source": [
        "------------------------"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4HCJqjXlIX4o",
        "colab_type": "code",
        "outputId": "5930de89-ed8c-4218-e704-b19cb1afb6a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "gm=KMeans(n_clusters=3,n_init=100,max_iter=300,random_state=17)\n",
        "y_means=gm.fit_predict(xpc)\n",
        "y_means\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 2, 2, 2], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXx6m6qKI2-7",
        "colab_type": "code",
        "outputId": "74252048-5feb-4392-a393-67c4886e5c1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        }
      },
      "source": [
        "plt.scatter(xpc[:,0],xpc[:,1],c=y_means, s=20, cmap='viridis');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ek7aSRE8JD9a",
        "colab_type": "code",
        "outputId": "f82cdf0a-a8c6-41db-af21-201fa11e7d27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        }
      },
      "source": [
        "tab = pd.crosstab(y,y_means, margins=True)\n",
        "tab.index = ['walking', 'going up the stairs',\n",
        "             'going down the stairs', 'sitting', 'standing', 'laying', 'all']\n",
        "tab.columns = ['cluster' + str(i + 1) for i in range(3)] + ['all']\n",
        "tab"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>cluster1</th>\n",
              "      <th>cluster2</th>\n",
              "      <th>cluster3</th>\n",
              "      <th>all</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>walking</th>\n",
              "      <td>0</td>\n",
              "      <td>524</td>\n",
              "      <td>1198</td>\n",
              "      <td>1722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>going up the stairs</th>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>1384</td>\n",
              "      <td>1544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>going down the stairs</th>\n",
              "      <td>0</td>\n",
              "      <td>862</td>\n",
              "      <td>544</td>\n",
              "      <td>1406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sitting</th>\n",
              "      <td>1769</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>standing</th>\n",
              "      <td>1904</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>laying</th>\n",
              "      <td>1923</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>1944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>all</th>\n",
              "      <td>5596</td>\n",
              "      <td>1546</td>\n",
              "      <td>3157</td>\n",
              "      <td>10299</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       cluster1  cluster2  cluster3    all\n",
              "walking                       0       524      1198   1722\n",
              "going up the stairs           0       160      1384   1544\n",
              "going down the stairs         0       862       544   1406\n",
              "sitting                    1769         0         8   1777\n",
              "standing                   1904         0         2   1906\n",
              "laying                     1923         0        21   1944\n",
              "all                        5596      1546      3157  10299"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "234da989840379595bc0e65f6c1f81b78eddf19f",
        "id": "6QBHLFGKqiVE",
        "colab_type": "text"
      },
      "source": [
        "Let's try another clustering algorithm, described in the article – agglomerative clustering."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "1b6a2ea741788687a5f650ae5489385ffa8246cf",
        "id": "1pTahpF-qiVE",
        "colab_type": "code",
        "outputId": "853f8846-8067-4241-94af-ebd33303e138",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        }
      },
      "source": [
        " ag = AgglomerativeClustering(n_clusters=n_classes, \n",
        "                              linkage='ward').fit(xpc)\n",
        "ag.labels_\n",
        "plt.scatter(xpc[:,0],xpc[:,1],c=ag.labels_, s=20, cmap='viridis');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MFL8KoTCLvX6",
        "colab_type": "code",
        "outputId": "04d1d23e-5a25-4d1d-eb66-a4576775aa5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        }
      },
      "source": [
        "tab = pd.crosstab(y,ag.labels_, margins=True)\n",
        "tab.index = ['walking', 'going up the stairs',\n",
        "             'going down the stairs', 'sitting', 'standing', 'laying', 'all']\n",
        "tab.columns = ['cluster' + str(i + 1) for i in range(6)] + ['all']\n",
        "tab"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>cluster1</th>\n",
              "      <th>cluster2</th>\n",
              "      <th>cluster3</th>\n",
              "      <th>cluster4</th>\n",
              "      <th>cluster5</th>\n",
              "      <th>cluster6</th>\n",
              "      <th>all</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>walking</th>\n",
              "      <td>0</td>\n",
              "      <td>540</td>\n",
              "      <td>1156</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>1722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>going up the stairs</th>\n",
              "      <td>0</td>\n",
              "      <td>145</td>\n",
              "      <td>1399</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>going down the stairs</th>\n",
              "      <td>0</td>\n",
              "      <td>727</td>\n",
              "      <td>536</td>\n",
              "      <td>0</td>\n",
              "      <td>143</td>\n",
              "      <td>0</td>\n",
              "      <td>1406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sitting</th>\n",
              "      <td>1587</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>106</td>\n",
              "      <td>0</td>\n",
              "      <td>84</td>\n",
              "      <td>1777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>standing</th>\n",
              "      <td>1866</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>1906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>laying</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1881</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>1944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>all</th>\n",
              "      <td>3461</td>\n",
              "      <td>1412</td>\n",
              "      <td>3091</td>\n",
              "      <td>1987</td>\n",
              "      <td>169</td>\n",
              "      <td>179</td>\n",
              "      <td>10299</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       cluster1  cluster2  cluster3  ...  cluster5  cluster6    all\n",
              "walking                       0       540      1156  ...        26         0   1722\n",
              "going up the stairs           0       145      1399  ...         0         0   1544\n",
              "going down the stairs         0       727       536  ...       143         0   1406\n",
              "sitting                    1587         0         0  ...         0        84   1777\n",
              "standing                   1866         0         0  ...         0        40   1906\n",
              "laying                        8         0         0  ...         0        55   1944\n",
              "all                        3461      1412      3091  ...       169       179  10299\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "e7865fcf352c96efef317c24b424233acd0c5dd3",
        "id": "Hke6cknZqiVH",
        "colab_type": "text"
      },
      "source": [
        "Calculate the Adjusted Rand Index (`sklearn.metrics`) for the resulting clustering and for ` KMeans` with the parameters from the 4th question.\n",
        "\n",
        "This is an opportunity for you to learn accuracy metric for Clustering. As opposed to classfication, it is difficult to assess the quality of results from clustering. Here, a metric cannot depend on the labels but only on the goodness of split. Secondly, we do not usually have true labels of the observations when we use clustering.\n",
        "\n",
        "sklearn.metrics have several implementations\n",
        "\n",
        "### [Adjusted Rand Index (ARI)](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html)\n",
        "ARI assumes that you know the labels, in this case we know the labels y. It measures how well are the cluster splits in terms of same label observations being together and different label obervations being in different cluster. Its value ranges between -1 and 1, with -1 being bad and 1 being best.\n",
        "\n",
        "### [Adjusted Mututal Information (AMI)](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html)\n",
        "It is similar to ARI but it uses entropy withing the clusters (if a cluster has multiple lables its entropy will be high). It ranges between 0 and 1\n",
        "\n",
        "### [Homogeneity (h), completeness (c), V-measure](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.v_measure_score.html)\n",
        "Name must make sense.  h  evaluates whether each cluster is composed of same class objects, and  c  measures how well the same class objects fit the clusters. \n",
        "V measure is combination of homogeniety and completeness (harmonic mean just as the F measure in precision and recall)\n",
        "\n",
        "### [Silhouette](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html)\n",
        "The silhouette distance shows to which extent the distance between the objects of the same class differ from the mean distance between the objects from different clusters. It ranges from -1 to 1 with -1 being bad and 1 being good. It does not assume that you know the labels and only depends upon the clusters. (different from the previous 3 measures)\n",
        "\n",
        "\n",
        "* Here you have to use ARI"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "6d934179759ebf8159a6879c6e906a6a4d44544e",
        "id": "FnMf2lbsqiVH",
        "colab_type": "code",
        "outputId": "01aff990-ad5b-46fd-a713-114abc002d22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "metrics.adjusted_rand_score(y,y_kmeans)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4192465241035231"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "grKU3Y-wOUGw",
        "colab_type": "code",
        "outputId": "8f182462-05cb-4f9f-9828-bf723a2849af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "metrics.adjusted_rand_score(y,ag.labels_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5177704539566773"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "ccee7b5d2d1155ebe672cec4282bb06af01f87a4",
        "id": "a8uidnTEqiVK",
        "colab_type": "text"
      },
      "source": [
        "**Question 6:** <br>\n",
        "Select all the correct statements. <br>\n",
        "\n",
        "**Answer options:**\n",
        "- According to ARI, KMeans handled clustering worse than Agglomerative Clustering \n",
        "- For ARI, it does not matter which tags are assigned to the cluster, only the partitioning of instances into clusters matters \n",
        "- In case of random partitioning into clusters, ARI will be close to zero "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "1367c265153403060e4e0bd69fe578cfafd03678",
        "id": "MDsZbCEfqiVL",
        "colab_type": "text"
      },
      "source": [
        "-------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "eadfcc38dded52cc91f75a422a6eff8f1a5993c7",
        "id": "lS31YzY5qiVM",
        "colab_type": "text"
      },
      "source": [
        "You can notice that the task is not very well solved when we try to detect several clusters (> 2). Now, let's solve the classification problem, given that the data is labeled.\n",
        "\n",
        "For classification, use the support vector machine – class `sklearn.svm.LinearSVC`. In this course, we did study this algorithm separately, but it is well-known and you can read about it, for example [here](http://cs231n.github.io/linear-classify/#svmvssoftmax).\n",
        "\n",
        "Choose the `C` hyperparameter for` LinearSVC` using `GridSearchCV`.\n",
        "\n",
        "- Train the new `StandardScaler` on the training set (with all original features), apply scaling to the test set\n",
        "- In `GridSearchCV`, specify `cv` = 3."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "61151d2f16a8beb49e7635e95ea55129f6e984ac",
        "id": "XQvaJk9nqiVN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # Your code here\n",
        " scaler = StandardScaler()\n",
        " X_train_scaled =scaler.fit_transform(X_train)\n",
        " X_test_scaled = scaler.fit_transform(X_test)\n",
        " "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "7b0c661771542832de4f923b5206f6c8df1aa508",
        "id": "BG7MXFwQqiVR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "svc = LinearSVC(random_state=RANDOM_STATE)\n",
        "svc_params = {'C': [0.001, 0.01, 0.1, 1, 10]}\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3S54OwDhkit",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "linearSVC = GridSearchCV(estimator=svc,param_grid=svc_params,cv=3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qN1a8PrhCFO",
        "colab_type": "code",
        "outputId": "91de23ef-d71e-49be-c368-4d513500f394",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        }
      },
      "source": [
        "linearSVC = linearSVC.fit(X_train_scaled, Y_train)\n",
        "best_params = linearSVC.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "3f0573b69142f89a7a4f4852c4a1366b7f85270b",
        "id": "yeGykSrhqiVT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "best_svc = linearSVC.best_estimator_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lt1_qUZuoAE8",
        "colab_type": "code",
        "outputId": "63c95898-c889-4481-a134-7ee3cb6b70df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "best_params[\"C\"]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "4cff688492768e0f226b0aff08d4eb07450ed4ce",
        "id": "wPgz5nSwqiVX",
        "colab_type": "code",
        "outputId": "b5cb77fd-cf8d-4e1a-e13f-9eaa6e013abf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        " best_svc.fit(X_train_scaled,Y_train)\n",
        " best_svc.score(X_train_scaled,Y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9945593035908596"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "b6214f9ec00411039cde3237ed7891053d9d8c5d",
        "id": "7qM51eqrqiVZ",
        "colab_type": "text"
      },
      "source": [
        "**Question 7**<br>\n",
        "Which value of the hyperparameter `C` was chosen the best on the basis of cross-validation? <br>\n",
        "\n",
        "**Answer options:**\n",
        "- 0.001\n",
        "- 0.01\n",
        "- 0.1 \n",
        "- 1\n",
        "- 10"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "02e74bf62e37fbad5df8a685d6b06b2c9a48daaf",
        "id": "MxwkBTsUqiVZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " y_predicted = best_svc.predict(X_test_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "3e9804082d8a9eb666149074a8d1983ccb19ae2d",
        "id": "fCp3X_XlqiVd",
        "colab_type": "code",
        "outputId": "3907c7ee-a975-4881-e2d6-31b932312985",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        }
      },
      "source": [
        " tab = pd.crosstab(Y_test, y_predicted, margins=True)\n",
        " tab.index = ['walking', 'climbing up the stairs',\n",
        "              'going down the stairs', 'sitting', 'standing', 'laying', 'all']\n",
        " tab.columns = ['walking', 'climbing up the stairs',\n",
        "              'going down the stairs', 'sitting', 'standing', 'laying', 'all']\n",
        " tab"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>walking</th>\n",
              "      <th>climbing up the stairs</th>\n",
              "      <th>going down the stairs</th>\n",
              "      <th>sitting</th>\n",
              "      <th>standing</th>\n",
              "      <th>laying</th>\n",
              "      <th>all</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>walking</th>\n",
              "      <td>479</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>climbing up the stairs</th>\n",
              "      <td>4</td>\n",
              "      <td>465</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>going down the stairs</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>416</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sitting</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>433</td>\n",
              "      <td>52</td>\n",
              "      <td>2</td>\n",
              "      <td>491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>standing</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>516</td>\n",
              "      <td>0</td>\n",
              "      <td>532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>laying</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>531</td>\n",
              "      <td>537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>all</th>\n",
              "      <td>484</td>\n",
              "      <td>482</td>\n",
              "      <td>421</td>\n",
              "      <td>453</td>\n",
              "      <td>574</td>\n",
              "      <td>533</td>\n",
              "      <td>2947</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        walking  climbing up the stairs  ...  laying   all\n",
              "walking                     479                      11  ...       0   496\n",
              "climbing up the stairs        4                     465  ...       0   471\n",
              "going down the stairs         1                       2  ...       0   420\n",
              "sitting                       0                       4  ...       2   491\n",
              "standing                      0                       0  ...       0   532\n",
              "laying                        0                       0  ...     531   537\n",
              "all                         484                     482  ...     533  2947\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VP5lBA3z0hkl",
        "colab_type": "code",
        "outputId": "d40e233f-ffdd-48ef-de6c-e5585f2371cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        }
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y_test,y_predicted))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.99      0.97      0.98       496\n",
            "           2       0.96      0.99      0.98       471\n",
            "           3       0.99      0.99      0.99       420\n",
            "           4       0.96      0.88      0.92       491\n",
            "           5       0.90      0.97      0.93       532\n",
            "           6       1.00      0.99      0.99       537\n",
            "\n",
            "    accuracy                           0.96      2947\n",
            "   macro avg       0.97      0.96      0.96      2947\n",
            "weighted avg       0.96      0.96      0.96      2947\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "f1bae145f1034a9c42679e73240cd0860bc207c6",
        "id": "CNC0TXdqqiVg",
        "colab_type": "text"
      },
      "source": [
        "**Question 8:** <br>\n",
        "Which activity type is worst detected by SVM in terms of precision? Recall?<br>\n",
        "\n",
        "**Answer options:**\n",
        "- precision – going up the stairs, recall – laying\n",
        "- precision – laying, recall – sitting\n",
        "- precision – walking, recall – walking\n",
        "- precision – standing, recall – sitting "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCQ3MtUj38Tm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pca=PCA(n_components=66,random_state=17)\n",
        "X_train_scaled_pc=pca.fit_transform(X_train_scaled)\n",
        "X_test_scaled_pc=pca.fit_transform(X_test_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUYDsMbf5M3F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "svcp = LinearSVC(random_state=RANDOM_STATE)\n",
        "svcp_params = {'C': [0.001, 0.01, 0.1, 1, 10]}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ZOOT0j-5YtO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lineaSVC = GridSearchCV(estimator=svcp,param_grid=svcp_params,cv=3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgyvVKfQ5m6p",
        "colab_type": "code",
        "outputId": "56eed2b9-a7e2-4af2-b65b-cd0d81c089f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        }
      },
      "source": [
        "lineaSVC = linearSVC.fit(X_train_scaled_pc, Y_train)\n",
        "bestp_params = lineaSVC.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBJLC-YB54Z_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bestp_svc = lineaSVC.best_estimator_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jM5QFGD96AXb",
        "colab_type": "code",
        "outputId": "1dc8a4f3-3df1-467d-8e96-4e2f24d74747",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "bestp_params[\"C\"]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWax_lmN6F-Q",
        "colab_type": "code",
        "outputId": "33c042eb-2192-4673-af44-9915e35790ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        " bestp_svc.fit(X_train_scaled_pc,Y_train)\n",
        " bestp_svc.score(X_train_scaled_pc,Y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9587867247007617"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "472b5dadcd567b019cc6cd729a3917459207df14",
        "id": "_ipH-YdAqiVh",
        "colab_type": "text"
      },
      "source": [
        "Finally, do the same thing as in Question 7, but add PCA.\n",
        "\n",
        "- Use `X_train_scaled` and` X_test_scaled`\n",
        "- Train the same PCA as before, on the scaled training set, apply scaling to the test set\n",
        "- Choose the hyperparameter `C` via cross-validation on the training set with PCA-transformation. You will notice how much faster it works now.\n",
        "\n",
        "**Question 9:** <br>\n",
        "What is the difference between the best quality (accuracy) for cross-validation in the case of all 561 initial characteristics and in the second case, when the principal component method was applied? Round to the nearest percent. <br>\n",
        "\n",
        "**Options:**\n",
        "- quality is the same\n",
        "- 2%\n",
        "- 4%\n",
        "- 10%\n",
        "- 20%"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "51508ed1cc529287af7cb3ebe62c5112bd4e5d19",
        "id": "W7ZLRsMxqiVh",
        "colab_type": "code",
        "outputId": "f88d3853-44ce-41af-b436-d48031ca1454",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "best_svc.score(X_train_scaled,Y_train)-bestp_svc.score(X_train_scaled_pc,Y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.035772578890097995"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "01dc096cb4ea5717d8709f2a7581574211753cf8",
        "id": "Jq5_Yq9UqiVk",
        "colab_type": "text"
      },
      "source": [
        "**Question 10:** <br>\n",
        "Select all the correct statements:\n",
        "\n",
        "**Answer options:**\n",
        "- Principal component analysis in this case allowed to reduce the model training time, while the quality (mean cross-validation accuracy) suffered greatly, by more than 10%\n",
        "- PCA can be used to visualize data, but there are better methods for this task, for example, [tSNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html). However, PCA has lower computational complexity \n",
        "- PCA builds linear combinations of initial features, and in some applications they might be poorly interpreted by humans "
      ]
    }
  ]
}