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    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "%matplotlib inline"
      ]
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      "source": [
        "\n# PCA example with Iris Data-set\n\nPrincipal Component Analysis applied to the Iris dataset.\n\nSee `here <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more\ninformation on this dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      },
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      "source": [
        "# Code source: Ga\u00ebl Varoquaux\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\nfrom sklearn import decomposition\nfrom sklearn import datasets\n\nnp.random.seed(5)\n\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\n\nfig = plt.figure(1, figsize=(4, 3))\nplt.clf()\nax = Axes3D(fig, rect=[0, 0, 0.95, 1], elev=48, azim=134)\n\nplt.cla()\npca = decomposition.PCA(n_components=3)\npca.fit(X)\nX = pca.transform(X)\n\nfor name, label in [(\"Setosa\", 0), (\"Versicolour\", 1), (\"Virginica\", 2)]:\n    ax.text3D(\n        X[y == label, 0].mean(),\n        X[y == label, 1].mean() + 1.5,\n        X[y == label, 2].mean(),\n        name,\n        horizontalalignment=\"center\",\n        bbox=dict(alpha=0.5, edgecolor=\"w\", facecolor=\"w\"),\n    )\n# Reorder the labels to have colors matching the cluster results\ny = np.choose(y, [1, 2, 0]).astype(float)\nax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.nipy_spectral, edgecolor=\"k\")\n\nax.w_xaxis.set_ticklabels([])\nax.w_yaxis.set_ticklabels([])\nax.w_zaxis.set_ticklabels([])\n\nplt.show()"
      ]
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