powerlaws | Java library for the analysis of power law distributed data | Data Visualization library

by Data2Semantics Java Version: v0.1.0 License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | powerlaws Summary

powerlaws is a Java library typically used in Analytics, Data Visualization applications. powerlaws has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However powerlaws build file is not available. You can download it from GitHub.

This is a small java library for the analysis of power law distributed data. The methods implemented are all taken form the paper Power-Law Distributions in Empirical Data by Clauset, Shalizi and Newman (2007) and its reference implementations available at It contains facilities for estimating parameters, uncertainty and significance and for generating power law distributed data. All methods are implemented for continuous data, discrete data and the approximation of discrete data with a continuous distribution.

Support

Quality

Security

License

Reuse

Support

powerlaws has a low active ecosystem.

It has 18 star(s) with 9 fork(s). There are 3 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 1 have been closed. On average issues are closed in 120 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of powerlaws is v0.1.0

Quality

powerlaws has 0 bugs and 0 code smells.

Security

powerlaws has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

powerlaws code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

powerlaws is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

powerlaws releases are available to install and integrate.

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

Installation instructions, examples and code snippets are available.

powerlaws saves you 501 person hours of effort in developing the same functionality from scratch.

It has 1178 lines of code, 114 functions and 15 files.

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

Top functions reviewed by kandi - BETA

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

Calculates Uncertainties for a given set of data
Returns the standard deviation of the list of numbers
Returns a series of integers starting from a specific value
Returns the mean value of a list of numbers
Calculates Uncertainties from the given data
Creates a fit from a collection
Runs the KDF test
Returns a cumulative histogram of data
Tests a set of integers
Walks through a series of Data
Estimates the uncertainties of the given data
Computes the cumulative distribution function
Returns the probability of x
Computes the powerLaw with the given dataSamples
Computes the power of an integer
Fits the distribution with the given dataSamples
Generate integer
Computes the PowerLaw with the given dataSamples
Calculates the cumulative probability of the given data
Generates a random value
Calculates the CDF of the given integer
Returns the cumulative probability of the given integer
Returns the probability of the given x
Sorts the collection
Generates a list of points
Compares two numbers

Get all kandi verified functions for this library.

powerlaws Key Features

No Key Features are available at this moment for powerlaws.

powerlaws Examples and Code Snippets

No Code Snippets are available at this moment for powerlaws.

Community Discussions

Trending Discussions on powerlaws

Continuous distribution in scipy that looks like two powerlaws?

QUESTION

Continuous distribution in scipy that looks like two powerlaws?

Asked 2022-Mar-06 at 00:14

I need to generate random samples from a distribution that looks roughly like this:

To generate the image above I combined two powerlaws:

...

ANSWER

Answered 2022-Mar-06 at 00:14

In this case, the answer is simple.

The integral of a * x**(a - 1) + b * x**(b - 1) over the interval [0, 1] is simply 2, suggesting that this distribution is a mixture of two power-law distributions, one of which is drawn with the same probability as the other.* Specifically, your distribution has the following density function:

(a * x**(a - 1))/2 + (b * x**(b - 1))/2.

Then, a simple algorithm to sample from this distribution is:

Generate u, a uniform random variate in [0, 1].
With probability 1/2, return u**(1/a). Otherwise, return u**(1/b).

Code follows:

Source https://stackoverflow.com/questions/71363305

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

Vulnerabilities

No vulnerabilities reported

Install powerlaws

You can use [jitpack.io](https://jitpack.io/#Data2Semantics/powerlaws) to add the powerlaws library to your project. Simply add the following repository to your pom.xml:. And the following dependency. Check [the releases](https://github.com/Data2Semantics/powerlaws/releases) for the version number of the latest release.

Support

For bug fixes and suggestions, GitHub is preferred (send pbloem a message or create a ticket). If you don’t have a GitHub account you can email to p & peterbloem & nl. (replacing the ampersands with an at symbol and a dot respectively).

Find more information at: