slice-sampling | If you want to run it yourself , install Bokeh

by JannerM Python Version: Current License: No License

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kandi X-RAY | slice-sampling Summary

slice-sampling is a Python library. slice-sampling has no bugs, it has no vulnerabilities and it has low support. However slice-sampling build file is not available. You can download it from GitHub.

If you want to run it yourself, install Bokeh and then $ ./run.sh.

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Quality

Security

License

Reuse

Support

slice-sampling has a low active ecosystem.

It has 3 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

slice-sampling has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of slice-sampling is current.

Quality

slice-sampling has 0 bugs and 0 code smells.

Security

slice-sampling has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

slice-sampling code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

slice-sampling does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

slice-sampling releases are not available. You will need to build from source code and install.

slice-sampling has no build file. You will be need to create the build yourself to build the component from source.

It has 192 lines of code, 13 functions and 1 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed slice-sampling and discovered the below as its top functions. This is intended to give you an instant insight into slice-sampling implemented functionality, and help decide if they suit your requirements.

Performs a step out of the grid
Refreshes the data source
Compute the value of the fit
Refresh the figure
Update the next step
Run next step
Sample from the slice
Called when a radio button is received
Change update function
Double double

Get all kandi verified functions for this library.

slice-sampling Key Features

No Key Features are available at this moment for slice-sampling.

slice-sampling Examples and Code Snippets

No Code Snippets are available at this moment for slice-sampling.

Community Discussions

Trending Discussions on slice-sampling

Which hyperparameter optimization technique is used in Mallet for LDA?

QUESTION

Which hyperparameter optimization technique is used in Mallet for LDA?

Asked 2021-May-21 at 13:47

I am wondering which technique is used to learn the Dirichlet priors in Mallet's LDA implementation.

Chapter 2 of Hanna Wallach's Ph.D. thesis gives a great overview and a valuable evaluation of existing and new techniques to learn the Dirichlet priors from the data.

Tom Minka initially provided his famous fixed-point iteration approach, however without any evaluation or recommendations.

Furthermore, Jonathan Chuang did some comparisons between previously proposed methods, including the Newton−Raphson method.

LiangJie Hong says the following in his blog:

A typical approach is to utilize Monte-Carlo EM approach where E-step is approximated by Gibbs sampling while M-step is to perform a gradient-based optimization approach to optimize Dirichlet parameters. Such approach is implemented in Mallet package.

Mallet mentions the Minka's fixed-point iterations with and without histograms.

However, the method that is actually used simply states:

Learn Dirichlet parameters using frequency histograms

Could someone provide any reference that describes the used technique?

...

ANSWER

Answered 2021-May-21 at 13:47

It uses the fixed point iteration. The frequency histograms method is just an efficient way to calculate it. They provide an algebraically equivalent way to do the exact same computation. The update function consists of a sum over a large number of Digamma functions. This function by itself is difficult to compute, but the difference between two Digamma functions (where the arguments differ by an integer) is relatively easy to compute, and even better, it "telescopes" so that the answer to Digamma(a + n) - Digamma(a) is one operation away from the answer to Digamma(a + n + 1) - Digamma(a). If you work through the histogram of counts from 1 to the max, adding up the number of times you saw a count of n at each step, the calculation becomes extremely fast. Initially, we were worried that hyperparameter optimization would take so long that no one would do it. With this trick it's so fast it's not really significant compared to the Gibbs sampling.

Source https://stackoverflow.com/questions/67622671

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install slice-sampling

You can download it from GitHub.
You can use slice-sampling like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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