![Gaussian mixture models in r Gaussian mixture models in r](https://scikit-learn.org/stable/_images/sphx_glr_plot_gmm_covariances_0011.png)
.PythonIn Greek mythology, Python is the name of a a huge serpent and sometimes a dragon. Python had been killed by the god Apollo at Delphi. Python was created out of the slime and mud left after the great flood. He was appointed by Gaia (Mother Earth) to guard the oracle of Delphi, known as Pytho.The programming language Python has not been created out of slime and mud but out of the programming language ABC. It has been devised by a Dutch programmer, named Guido van Rossum, in Amsterdam.Origins of PythonGuido van Rossum wrote the following about the origins of Python in a foreword for the book 'Programming Python' by Mark Lutz in 1996:'Over six years ago, in December 1989, I was looking for a 'hobby' programming project that would keep me occupied during the week around Christmas.
If you landed on this post, you probably already know what a Gaussian Mixture Model is, so I will avoid the general description of the this technique. But if you are not aware of the details, you can just see the GMM as a k-means which is able to form stretched clusters, like the ones you can see in Figure 2. A Gaussian mixture model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. One can think of mixture models as generalizing k-means clustering to incorporate information about the covariance structure of the data as well as the centers of the latent Gaussians.
My office (a government-run research lab in Amsterdam) would be closed, but I had a home computer, and not much else on my hands. I decided to write an interpreter for the new scripting language I had been thinking about lately: a descendant of ABC that would appeal to Unix/C hackers. I chose Python as a working title for the project, being in a slightly irreverent mood (and a big fan of Monty Python's Flying Circus).' This website is free of annoying ads. We want to keep it like this. You can help with your donation:Job Applicationbodenseo is looking for a new trainer and software developper. You need to live in Germany and know German.
import numpy as np y0 = np. Multivariatenormal ( 0, 0 2, 0 0, 0.1 , size = 50 ) y1 = np. Multivariatenormal ( 0, 0 0.1, 0 0, 2 , size = 50 ) y2 = np. Multivariatenormal ( 2, 2 2, - 1.5 - 1.5, 2 , size = 50 ) y3 = np. Multivariatenormal ( - 2, - 2 0.5, 0 0, 0.5 , size = 50 ) y = np. Vstack ( y0, y1, y2, y3 )Thus, there are 200 data vectors in total. The data looks as follows.