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https://github.com/siddhi-io/siddhi-execution-streamingml
Extension that performs streaming machine learning on event streams
https://github.com/siddhi-io/siddhi-execution-streamingml
deep-learning execution extension machine-learning ml online online-machine-learning siddhi
Last synced: about 1 month ago
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Extension that performs streaming machine learning on event streams
- Host: GitHub
- URL: https://github.com/siddhi-io/siddhi-execution-streamingml
- Owner: siddhi-io
- License: apache-2.0
- Created: 2016-11-18T09:58:01.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2022-02-15T08:30:45.000Z (almost 3 years ago)
- Last Synced: 2024-05-02T02:03:43.472Z (8 months ago)
- Topics: deep-learning, execution, extension, machine-learning, ml, online, online-machine-learning, siddhi
- Language: Java
- Homepage: https://siddhi-io.github.io/siddhi-execution-streamingml/
- Size: 9.33 MB
- Stars: 4
- Watchers: 31
- Forks: 30
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
Siddhi Execution Streaming ML
======================================
[](https://wso2.org/jenkins/job/siddhi/job/siddhi-execution-streamingml/)
[](https://github.com/siddhi-io/siddhi-execution-streamingml/releases)
[](https://github.com/siddhi-io/siddhi-execution-streamingml/releases)
[](https://github.com/siddhi-io/siddhi-execution-streamingml/issues)
[](https://github.com/siddhi-io/siddhi-execution-streamingml/commits/master)
[](https://opensource.org/licenses/Apache-2.0)
The **siddhi-execution-streamingml extension** is a Siddhi extension that provides streaming machine learning (clustering, classification and regression) on event streams.
For information on Siddhi and it's features refer Siddhi Documentation.
## Download
* Versions 2.x and above with group id `io.siddhi.extension.*` from here.
* Versions 1.x and lower with group id `org.wso2.extension.siddhi.*` from here.
## Latest API Docs
Latest API Docs is 2.0.5.
## Features
* bayesianRegression *(Stream Processor)*
This extension predicts using a Bayesian linear regression model.Bayesian linear regression allows determining the uncertainty of each prediction by estimating the full-predictive distribution
* kMeansIncremental *(Stream Processor)*
Performs K-Means clustering on a streaming data set. Data points can be of any dimension and the dimensionality is calculated from number of parameters. All data points to be processed by a query should be of the same dimensionality. The Euclidean distance is taken as the distance metric. The algorithm resembles Sequential K-Means Clustering at https://www.cs.princeton.edu/courses/archive/fall08/cos436/Duda/C/sk_means.htm
* kMeansMiniBatch *(Stream Processor)*
Performs K-Means clustering on a streaming data set. Data points can be of any dimension and the dimensionality is calculated from number of parameters. All data points to be processed in a single query should be of the same dimensionality. The Euclidean distance is taken as the distance metric. The algorithm resembles mini-batch K-Means. (refer Web-Scale K-Means Clustering by D.Sculley, Google, Inc.).
* perceptronClassifier *(Stream Processor)*
This extension predicts using a linear binary classification Perceptron model.
* updateBayesianRegression *(Stream Processor)*
This extension builds/updates a linear Bayesian regression model. This extension uses an improved version of stochastic variational inference.
* updatePerceptronClassifier *(Stream Processor)*
This extension builds/updates a linear binary classification Perceptron model.
## Dependencies
There are no other dependencies needed for this extension.
## Installation
For installing this extension on various siddhi execution environments refer Siddhi documentation section on adding extensions.
## Support and Contribution
* We encourage users to ask questions and get support via StackOverflow, make sure to add the `siddhi` tag to the issue for better response.
* If you find any issues related to the extension please report them on the issue tracker.
* For production support and other contribution related information refer Siddhi Community documentation.