LsqFit.jl
Simple curve fitting in Julia (by JuliaNLSolvers)
Stheno.jl
Probabilistic Programming with Gaussian processes in Julia (by JuliaGaussianProcesses)
| LsqFit.jl | Stheno.jl | |
|---|---|---|
| 2 | 2 | |
| 305 | 332 | |
| 1.0% | 0.0% | |
| 5.5 | 4.3 | |
| 6 months ago | 6 months ago | |
| Julia | Julia | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
LsqFit.jl
Posts with mentions or reviews of LsqFit.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-19.
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Function prediction using Julia?
I was experimenting with LsqFit (fits curves given a function and its parameters) and it does the job, although predictions are not as accurate as I would like. Have any of you run into the same problem and have a more suitable alternative?
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nice language
My impression is that it's hard to skate through the entire Julia ecosystem without seeing them used in some places (e.g. LsqFit just casually throws them into their basic docs), but I can't claim to have seen enough of the ecosystem to really say whether that's true as a whole.
Stheno.jl
Posts with mentions or reviews of Stheno.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-19.
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[Discussion] Can we train with multiple sources of data, some very reliable, others less so?
There are multiple ways people do this. For example, you could use something like factor analysis, where the factor loadings onto the latent "true" signal/factor are fixed based on what you (presumably) know about the empirical reliability/error variance and (potentially) bias in each observed signal. Then you do your modeling with the inferred latent "true" signal. See the second example here to see that sort of approach in the context of a gaussian process model.
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Function prediction using Julia?
Another, more flexible (nonparametric) alternative might be to try a gaussian process model - for example, using Stheno.
What are some alternatives?
When comparing LsqFit.jl and Stheno.jl you can also consider the following projects:
MLJ.jl - A Julia machine learning framework
GeoStats.jl - An extensible framework for geospatial data science and geostatistical modeling fully written in Julia
skbel - SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.
DPMMSubClusters.jl - Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)
Gumbi - Gaussian Process Model Building Interface