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    {
      "cell_type": "code",
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Making a Cylinder-Bell-Funnel dataset\n\n\nThis example shows how to generate a Cylinder-Bell-Funnel dataset. This\nsimulated dataset was introduced by N. Saito in his Ph.D. thesis entitled\n\"Local feature extraction and its application\". It is one of the most\nwell-known datasets in time series classification. It is implemented as\n:func:`pyts.datasets.make_cylinder_bell_funnel`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nfrom pyts.datasets import make_cylinder_bell_funnel\n\n\nX, y = make_cylinder_bell_funnel(n_samples=12, random_state=42)\n\nplt.figure(figsize=(12, 9))\nfor i, classe in enumerate(['cylinder', 'bell', 'funnel']):\n    plt.subplot(3, 1, i + 1)\n    for x in X[y == i]:\n        plt.plot(x, color='C0', linewidth=0.9)\n    plt.title('Class: {}'.format(classe), fontsize=16)\n\nplt.tight_layout()\nplt.subplots_adjust(hspace=0.4)\nplt.show()"
      ]
    }
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