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https://github.com/ap6yc/rocketeer.jl

A Julia implementation of the Rocket method of using random feature kernels for time series classification.

https://github.com/ap6yc/rocketeer.jl

Science Score: 36.0%

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    Found 8 DOI reference(s) in README
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Repository

A Julia implementation of the Rocket method of using random feature kernels for time series classification.

Basic Info
  • Host: GitHub
  • Owner: AP6YC
  • License: mit
  • Language: Julia
  • Default Branch: develop
  • Size: 543 KB
Statistics
  • Stars: 4
  • Watchers: 1
  • Forks: 0
  • Open Issues: 1
  • Releases: 4
Created almost 3 years ago · Last pushed over 2 years ago
Metadata Files
Readme Contributing License Code of conduct

README.md

rocket-header

A Julia implementation of the Rocket method of using random feature kernels for time series classification.

This project is not programmed by the original authors of the original paper; please see the Attribution section for more details on the original paper and software.

| Documentation | Coverage | CI Status | Releases | |:-----------------:|:------------:|:-------------:|:-----------:| | Dev | Codecov | CI Status | Zenodo | | Stable | Coveralls | Documentation | version | | Dependents | Issues | JuliaHub Status | Downloads | | deps | GitHubIssues | JuliaHub | Downloads |

Table of Contents

Usage

For detailed usage instructions, please see the Documentation.

To use the package, you must:

  1. Load Rocketeer,
  2. Create a RocketModule object (with optionally specified input_length and n_kernels hyperparameters),
  3. apply_kernels to your dataset to extract the Rocket features,
  4. Optionally save_rocket and load_rocket if you intend to utilize the exact same kernels in future experiments.

For example:

```julia

Load the module

using Rocketeer

Set some parameters of the example

filepath = "myrocket" # Point to a save file inputlength = 10 # The length of the input window n_kernels = 200 # The number of kernels to generate

Create a rocket module

myrocket = RocketModule(inputlength, n_kernels)

Save it for future use

saverocket(myrocket, filepath)

Load the module back into a new object

mynewrocket = load_rocket(filepath)

Create some random data

X = rand(input_length)

Apply the kernels to get features

features = applykernels(mynew_rocket, X) ```

Attribution

Authors

This Julia package is programmed by:

The original paper is authored by:

  • Angus Dempster
  • Francois Petitjean
  • Geoff Webb

The links for the original work are:

The bibtex entry for the original paper is:

bibtex @article{dempster_etal_2020, author = {Dempster, Angus and Petitjean, Francois and Webb, Geoffrey I}, title = {ROCKET: Exceptionally fast and accurate time classification using random convolutional kernels}, year = {2020}, journal = {Data Mining and Knowledge Discovery}, doi = {https://doi.org/10.1007/s10618-020-00701-z} }

Icons

The icon used for the project logo is from the following:

Owner

  • Name: Sasha Petrenko
  • Login: AP6YC
  • Kind: user

Graduate researcher of applied computational intelligence at the Missouri University of Science and Technology.

GitHub Events

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Top Authors
Issue Authors
  • AP6YC (5)
Pull Request Authors
  • AP6YC (8)
Top Labels
Issue Labels
enhancement (3) bug (2) documentation (1)
Pull Request Labels
enhancement (4) bug (3) release (1) documentation (1)

Packages

  • Total packages: 1
  • Total downloads: unknown
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 4
juliahub.com: Rocketeer

A Julia implementation of the Rocket method of using random feature kernels for time series classification.

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 0 Total
Rankings
Dependent repos count: 10.1%
Average: 24.1%
Dependent packages count: 38.1%
Last synced: 10 months ago

Dependencies

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  • julia-actions/julia-runtest latest composite
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  • styfle/cancel-workflow-action 0.11.0 composite
.github/workflows/CompatHelper.yml actions
.github/workflows/Documentation.yml actions
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  • julia-actions/setup-julia latest composite
  • styfle/cancel-workflow-action 0.9.1 composite
.github/workflows/TagBot.yml actions
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