t-Distributed Stochastic Neighbour Embedding (tSNE)
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 35/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- fsharp
- Domain
- machine-learning
Research direction
Start with the tSNE pseudocode, coding hints, and references in the linked project page. Prototype the dimensionality-reduction method in a notebook or script, then determine how it can be incorporated into FSharp.Stats. Done means the library provides tSNE functionality that produces a two- or three-dimensional representation of multidimensional data.
Written by the indexing model from the issue text.
Description
Description
tSNE is a dimensionality reduction method. It allows you to visualise a multi-dimensional dataset in 2 or 3 dimensional scatter plot.
- All necessary information with pseudocode and coding hints can be accessed in reference [1].
- Of course you can start developing in notebooks/scripts and afterwards we try to incorporate into the library.
References
- Dominant language
- F#
- Stars
- 228
- Forks
- 58
- Avg merge
- 2d 7h
- Merged PRs (30d)
- 1
Getting set up
- No Dockerfile or Docker Compose file
- Has a pull request template
- Read the contributing guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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