Hands-On Unsupervised Learning with Python by Giuseppe Bonaccorso

Author:Giuseppe Bonaccorso [Giuseppe Bonaccorso]
Language: eng
Format: epub
Tags: COM042000 - COMPUTERS / Natural Language Processing, COM044000 - COMPUTERS / Neural Networks, COM004000 - COMPUTERS / Intelligence (AI) and Semantics
Publisher: Packt Publishing
Published: 2019-02-27T14:21:09+00:00

nb_samples = 300

nb_centers = 2

X, Y = make_blobs(n_samples=nb_samples, n_features=2, center_box=[-1, 1], centers=nb_centers, cluster_std=[1.0, 0.6], random_state=1000)

The dataset has been generated by sampling from two Gaussian distributions with different standard deviations (1.0 and 0.6), and it is shown in the following screenshot:

Dataset for the Gaussian mixture example

Our goal is to employ both a Gaussian mixture model and k-means, and compare the final results. As we expect two components, the approximation of the data-generating process is as follows:

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