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stream-outlier | Anomaly detection for multiple data streams | Predictive Analytics library

 by   yxjiang Java Version: Current License: No License

 by   yxjiang Java Version: Current License: No License

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kandi X-RAY | stream-outlier Summary

stream-outlier is a Java library typically used in Analytics, Predictive Analytics, Spark applications. stream-outlier has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
Real Time Anomaly Detection Framework.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • stream-outlier has a low active ecosystem.
  • It has 12 star(s) with 1 fork(s). There are 3 watchers for this library.
  • It had no major release in the last 12 months.
  • stream-outlier has no issues reported. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of stream-outlier is current.
stream-outlier Support
Best in #Predictive Analytics
Average in #Predictive Analytics
stream-outlier Support
Best in #Predictive Analytics
Average in #Predictive Analytics

quality kandi Quality

  • stream-outlier has 0 bugs and 0 code smells.
stream-outlier Quality
Best in #Predictive Analytics
Average in #Predictive Analytics
stream-outlier Quality
Best in #Predictive Analytics
Average in #Predictive Analytics

securitySecurity

  • stream-outlier has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • stream-outlier code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
stream-outlier Security
Best in #Predictive Analytics
Average in #Predictive Analytics
stream-outlier Security
Best in #Predictive Analytics
Average in #Predictive Analytics

license License

  • stream-outlier 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.
stream-outlier License
Best in #Predictive Analytics
Average in #Predictive Analytics
stream-outlier License
Best in #Predictive Analytics
Average in #Predictive Analytics

buildReuse

  • stream-outlier releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • stream-outlier saves you 1054 person hours of effort in developing the same functionality from scratch.
  • It has 2389 lines of code, 132 functions and 46 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
stream-outlier Reuse
Best in #Predictive Analytics
Average in #Predictive Analytics
stream-outlier Reuse
Best in #Predictive Analytics
Average in #Predictive Analytics
Top functions reviewed by kandi - BETA

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

  • Calculate the minimum and max of the matrix .
    • Runs the benchmark .
      • Synchronized .
        • Calculate the scores for the given observation list
          • Generate a Multivariate Normal Distribution
            • Bump a tuple at index
              • Identifies the boundaries of the stream .
                • Convert the message to a tuple .
                  • Utility method to print out observation packages .
                    • Partition a list by a comparator .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      stream-outlier Key Features

                      Anomaly detection for multiple data streams.

                      stream-outlier Examples and Code Snippets

                      No Code Snippets are available at this moment for stream-outlier.

                      See all Code Snippets related to Predictive Analytics

                      Community Discussions

                      Trending Discussions on Predictive Analytics
                      • will TensorFlow utilize GPU for predictive Analysis?
                      • Restructuring Pandas Dataframe for large number of columns
                      • Display data from two json files in react native
                      Trending Discussions on Predictive Analytics

                      QUESTION

                      will TensorFlow utilize GPU for predictive Analysis?

                      Asked 2020-Nov-21 at 21:35

                      GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?

                      note: predictive Analysis requires no graphics processing.

                      ANSWER

                      Answered 2020-Nov-21 at 21:35
                      The short answer: yes, it will! The slightly longer answer:

                      The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.

                      The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.

                      If you want to know more about this, I'd recommend you start here or here.

                      some things to consider:
                      • how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
                      • Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
                      • Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.

                      Source https://stackoverflow.com/questions/64948197

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

                      Vulnerabilities

                      No vulnerabilities reported

                      Install stream-outlier

                      You can download it from GitHub.
                      You can use stream-outlier like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the stream-outlier component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                      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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