by design lazero can be deeply aligned, inspecting and studying user’s actions. it also has its own exploration space. however, these expectations can never be fully satisfied at the same time. if you want more power, you have to let go.
All models that support labeled data support semi-supervised learning, including naive Bayes classifiers, general Bayes classifiers, and hidden Markov models.
While probability Distributions are frequently used as components of more complex models such as mixtures and hidden Markov models, they can also be used by themselves. Many data science tasks require fitting a distribution to data or generating samples under a distribution. pomegranate has a large library of both univariate and multivariate distributions which can be used with an intuitive interface.
General Mixture Models (GMMs) are an unsupervised probabilistic model composed of multiple distributions (commonly referred to as components) and corresponding weights. This allows you to model more complex distributions corresponding to a singular underlying phenomena. For a full tutorial on what a mixture model is and how to use them, see the above tutorial.
import { python } from'pythonia' // Import tkinter const tk = awaitpython('tkinter') // All Python API access must be prefixed with await const root = await tk.Tk() // A function call with a $ suffix will treat the last argument as a kwarg dict const a = await tk.Label$(root, { text: 'Hello World' }) await a.pack() await root.mainloop() python.exit() // Make sure to exit Python in the end to allow node to exit. You can also use process.exit.