tensortools | barebones tensor decomposition library for CP decomposition

by ahwillia Python Version: 0.3 License: MIT

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kandi X-RAY | tensortools Summary

tensortools is a Python library. tensortools has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install tensortools' or download it from GitHub, PyPI.

TensorTools is a bare bones Python package for fitting and visualizing canonical polyadic (CP) tensor decompositions of higher-order data arrays. I originally developed this library for applications in neuroscience (Williams et al., 2018), but the code could be helpful in other domains.

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tensortools has a low active ecosystem.

It has 102 star(s) with 38 fork(s). There are 12 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 16 have been closed. On average issues are closed in 27 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensortools is 0.3

Quality

tensortools has 0 bugs and 0 code smells.

Security

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

tensortools code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

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

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

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tensortools releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

tensortools saves you 366 person hours of effort in developing the same functionality from scratch.

It has 874 lines of code, 45 functions and 23 files.

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

Top functions reviewed by kandi - BETA

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

Fits a shift_cp2
Predict the result of a function
Prevent zeros in x
Fit a shift using the shift function
Perform multishift hals
Return the norm of the factor
Fit a single shift to a template
Performs multishift prediction
Simulate a shifted CP
Predict the model
Linearize SSP problem
R Predictive predict function
Plot similarity plot
Generate a tensor with exponential random elements
Plot objective function
Plot the result
Generate a random tensor
Simulate a multishift model
Fits the model
Fit the shift_cp1_cp
Compute the NCP coefficient descent
Fit a single model to the shifted distribution
Perform the N - polynomial Hals algorithm
Plot factors
Computes the CP_AL operator
Optimized MCP_AL operator
Aligns the model ranks

Get all kandi verified functions for this library.

tensortools Key Features

No Key Features are available at this moment for tensortools.

tensortools Examples and Code Snippets

No Code Snippets are available at this moment for tensortools.

Community Discussions

No Community Discussions are available at this moment for tensortools.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install tensortools

From the command line run:. (You will need to have git installed for this command to work.).
Here's how to perform a parameter sweep over 1 - 9 components, and plot the reconstruction error and similarity diagnostics as a function of the model rank (these diagnostics are described in Williams et al., 2018). The snippet also uses plot_factors(...) to plot the factors extracted by one of the models in the ensemble. The method "ncp_hals" fits a nonnegative tensor decomposition, other methods are "ncp_bcd" (also nonnegative) and "cp_als" (unconstrained decomposition). See the tensortools/optimize/ folder for the implementation of these algorithms. Check out the scripts in the examples/ folder for other short demos.

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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