SnFFT: A Julia Toolkit for Fourier Analysis of Functions over Permutations
Authors: Gregory Plumb, Deepti Pachauri, Risi Kondor, Vikas Singh
JMLR 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | While the potential applicability of Sn FFT is fairly broad, as an example, we show how it can be used for clustering ranked data, where each ranking is modeled as a distribution on Sn. In Sn FFT, only a few lines of code are needed to compute the Fourier transforms, convert them into a data matrix, and pass the data matrix to R s sparcl library to perform this clustering. The details of the process can be found in the code of example clustering(). |
| Researcher Affiliation | Academia | Gregory Plumb EMAIL Department of Computer Sciences University of Wisconsin-Madison Madison, WI 53706 USA; Deepti Pachauri EMAIL Department of Computer Sciences University of Wisconsin-Madison Madison, WI 53706 USA; Risi Kondor EMAIL Department of Biostatistics & Med. Info. University of Chicago Chicago, IL 60637 USA; Vikas Singh EMAIL Department of Statistics University of Wisconsin-Madison Madison, WI 53706 USA |
| Pseudocode | No | The paper describes the software library's functionality and mathematical background but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Sn FFT is an easy to use software library written in the Julia language to facilitate Fourier analysis on the symmetric group (set of permutations) of degree n, denoted Sn and make it more easily deployable within statistical machine learning algorithms. ... The toolkit and the required documentation is available at: https://github.com/GDPlumb/Sn FFT.jl/. |
| Open Datasets | No | The paper mentions 'clustering ranked data' and describes how rankings can be modeled, but it does not provide specific access information (links, DOIs, repository names, or explicit citations for a dataset) for any publicly available or open dataset used in their example or experiments. |
| Dataset Splits | No | The paper does not mention specific dataset splits (e.g., percentages, sample counts, or defined methodologies for training/validation/test sets) for any dataset, as it does not provide concrete access information for a specific dataset itself. |
| Hardware Specification | No | The paper mentions supporting 'a multi-core cluster' and 'multiple processes spread across a cluster' but does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper states that 'Sn FFT is implemented in a high-level programming language called Julia' and that it uses 'R s sparcl library' for clustering, but it does not provide specific version numbers for Julia, R, or the sparcl library. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings. For the clustering example, it defers details to 'the code of example clustering().' |