plot_cluster_iris.ipynb

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        "%matplotlib inline"
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        "\n# K-means Clustering\n\n\nThe plots display firstly what a K-means algorithm would yield\nusing three clusters. It is then shown what the effect of a bad\ninitialization is on the classification process:\nBy setting n_init to only 1 (default is 10), the amount of\ntimes that the algorithm will be run with different centroid\nseeds is reduced.\nThe next plot displays what using eight clusters would deliver\nand finally the ground truth.\n\n\n"
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        "print(__doc__)\n\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n# Though the following import is not directly being used, it is required\n# for 3D projection to work\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfrom sklearn.cluster import KMeans\nfrom sklearn import datasets\n\nnp.random.seed(5)\n\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\n\nestimators = [('k_means_iris_8', KMeans(n_clusters=8)),\n              ('k_means_iris_3', KMeans(n_clusters=3)),\n              ('k_means_iris_bad_init', KMeans(n_clusters=3, n_init=1,\n                                               init='random'))]\n\nfignum = 1\ntitles = ['8 clusters', '3 clusters', '3 clusters, bad initialization']\nfor name, est in estimators:\n    fig = plt.figure(fignum, figsize=(4, 3))\n    ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n    est.fit(X)\n    labels = est.labels_\n\n    ax.scatter(X[:, 3], X[:, 0], X[:, 2],\n               c=labels.astype(np.float), edgecolor='k')\n\n    ax.w_xaxis.set_ticklabels([])\n    ax.w_yaxis.set_ticklabels([])\n    ax.w_zaxis.set_ticklabels([])\n    ax.set_xlabel('Petal width')\n    ax.set_ylabel('Sepal length')\n    ax.set_zlabel('Petal length')\n    ax.set_title(titles[fignum - 1])\n    ax.dist = 12\n    fignum = fignum + 1\n\n# Plot the ground truth\nfig = plt.figure(fignum, figsize=(4, 3))\nax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n\nfor name, label in [('Setosa', 0),\n                    ('Versicolour', 1),\n                    ('Virginica', 2)]:\n    ax.text3D(X[y == label, 3].mean(),\n              X[y == label, 0].mean(),\n              X[y == label, 2].mean() + 2, name,\n              horizontalalignment='center',\n              bbox=dict(alpha=.2, edgecolor='w', facecolor='w'))\n# Reorder the labels to have colors matching the cluster results\ny = np.choose(y, [1, 2, 0]).astype(np.float)\nax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y, edgecolor='k')\n\nax.w_xaxis.set_ticklabels([])\nax.w_yaxis.set_ticklabels([])\nax.w_zaxis.set_ticklabels([])\nax.set_xlabel('Petal width')\nax.set_ylabel('Sepal length')\nax.set_zlabel('Petal length')\nax.set_title('Ground Truth')\nax.dist = 12\n\nfig.show()"
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