LDPS | Learning DTW-Preserving Shapelets | Machine Learning library

by rtavenar Python Version: Current License: No License

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

kandi X-RAY | LDPS Summary

LDPS is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. LDPS has no bugs, it has no vulnerabilities and it has low support. However LDPS build file is not available. You can download it from GitHub.

This code is used to learn Shapelet features from time series that form an embedding such that L2-norm in the Shapelet Transform space is close to DTW between original time series.

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Quality

Security

License

Reuse

Support

LDPS has a low active ecosystem.

It has 12 star(s) with 11 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 11 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of LDPS is current.

Quality

LDPS has no bugs reported.

Security

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

License

LDPS 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

LDPS releases are not available. You will need to build from source code and install.

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

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Runs the fit method
Calculate the distance between the indices of the given indices
Compute the shapelet transform
Compute the distance between i and j
Perform partial fit
Calculates the shapelets for each iteration
Estimate the loss and the distance between each pair
Compute the loss of the loss function
Compute the distance between two shapes
Load a dataset
Calculate the loss
Loads a pickle file
Load precomputed distances from a file
Load a pickled distribution
Precompute the distances between the features
Dump to a file without dependencies
Fit the model to data
Calculate the shapelets for each iteration
Gradient of the gradient
Perform a partial fit

Get all kandi verified functions for this library.

LDPS Key Features

No Key Features are available at this moment for LDPS.

LDPS Examples and Code Snippets

No Code Snippets are available at this moment for LDPS.

Community Discussions

Trending Discussions on LDPS

XSLT - Create an array of attribute values from elements with the same attribute names

QUESTION

XSLT - Create an array of attribute values from elements with the same attribute names

Asked 2020-Jan-06 at 11:40

I have a XML document such as:

...

ANSWER

Answered 2020-Jan-06 at 11:40

I would add a template for listing the sub-cubes then apply those templates in your main for-each loop

Source https://stackoverflow.com/questions/59305554

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

Vulnerabilities

No vulnerabilities reported

Install LDPS

You can download it from GitHub.
You can use LDPS 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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