https://github.com/kiranvad/Amplitude-Phase-Distance

A light-weight repository to compute Amplitude Phase distance between two functions
https://github.com/kiranvad/Amplitude-Phase-Distance

clustering-algorithm differential-geometry functional-data-analysis hilbert-spaces machine-learning metric-learning

Last synced: 3 months ago
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A light-weight repository to compute Amplitude Phase distance between two functions

• Host: GitHub
• URL: https://github.com/kiranvad/Amplitude-Phase-Distance
• Owner: kiranvad
• License: gpl-3.0
• Created: 2022-11-26T23:13:37.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-28T23:13:30.000Z (8 months ago)
• Last Synced: 2024-08-02T16:34:30.139Z (6 months ago)
• Topics: clustering-algorithm, differential-geometry, functional-data-analysis, hilbert-spaces, machine-learning, metric-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 335 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Amplitude-Phase-Distance

A light-weight repository to compute Amplitude Phase distance between two functions

## Installation

1. Installation of this package is done in two steps. First, install the package using the following:

```bash

pip install git+https://github.com/kiranvad/Amplitude-Phase-Distance.git

```

2. Now, install a Cython-based package called warping:

```bash

pip install git+https://github.com/kiranvad/warping.git

```

You should now be able to run the [example](/example.ipynb)

## Notes

This package is intended to be used as a faster alternative to original works in [fdasrsf-python](https://github.com/jdtuck/fdasrsf_python).

If you used this package, please cite the original `fdasrsf-python` package using the following:

```bib

@phdthesis{tucker2014functional,

  title={Functional component analysis and regression using elastic methods},

  author={Tucker, J Derek},

  year={2014},

  school={The Florida State University}

}

```

For applications in materials science-related problems, you can look at the following papers or repositories linked there:

1. Vaddi, Kiran, Huat Thart Chiang, and Lilo D. Pozzo. "Autonomous retrosynthesis of gold nanoparticles via spectral shape matching." Digital Discovery (2022).

2. Lachowski, Kacper J., et al. "Multivariate analysis of peptide-driven nucleation and growth of Au nanoparticles." Digital Discovery (2022).

3. Vaddi, Kiran, Karen Li, and Lilo D. Pozzo. "Metric geometry tools for automatic structure phase map generation." Digital Discovery 2.5 (2023): 1471-1483.