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mathematicaforprediction's Introduction

Mission statement

This open source project is for Mathematica (Wolfram Language) implementations of statistical and Machine Learning algorithms that can be used for data analysis, forecast, prediction, and recommendation systems.

License matters

All code files and executable documents are with the license GPL 3.0. For details see http://www.gnu.org/licenses/ .

All documents are with the license Creative Commons Attribution 4.0 International (CC BY 4.0). For details see https://creativecommons.org/licenses/by/4.0/ .

Organization

The algorithms implementations are given in Mathematica package files ("*.m").

Explanations or presentations about the algorithms are given in Mathematica notebook files ("*.nb"), in PDF files, or in Markdown files.

Here are some fairly unique to the Mathematica / WL landscape algorithms:

The implemented algorithms are (usually) well documented. There is a fair amount of documents with related applications. There are also monadic programming implementations closely related to the "main directory" packages.

Some of the packages listed above have:

(The code in the R directory in this repository though is not updated, it is just kept for references. See the corresponding, actively worked on, dedicated repository R-packages.)

Associated blog (at WordPress)

There is a blog associated with this project, see MathematicaForPrediction at WordPress.


Support & appreciation

"Buy Me A Coffee"

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Anton Antonov
04.07.2013, Florida, USA
11.01.2017, Florida, USA (updated)
09.17.2019, Florida, USA (updated)
29.10.2022, Florida, USA (updated)

mathematicaforprediction's People

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mathematicaforprediction's Issues

QuantileEnvelope example

Could you provide an example of how to use QuantileEnvelope or QuantileEnvelopeRegion similar to what you did here?

BestFitParameters

Hello, Anton
I am using your QuantileRegression.m package in Wolfram Language to make MonteCarlo simulations and compare the results of residual analysis from Ordinary Least Squares.

Thank you for writing this package and making it available.

To automate this simulation, I need the ability to extract the parameters from the QuantileRegressionFit, similar to ["BestFitParameters"] in LinearModelFit.
Is there a way to do this?
Please let me know. I would greatly appreciate your response.
Thank you in advance,
Thad

LinearSolve::luc: Result for LinearSolve of badly conditioned matrix {{7.61902,0.0118411,0.00677937,0.10901,0.0815707,0.0166805,0.184858,0.0340636,0.0288654,0.0182311,<<246>>},<<9>>,<<246>>} may contain significant numerical errors.

Thanks for creating these Mathematica packages.
When I run NMF on a GPT-2 embedding matrix, I get warnings from LinearSolve.
I tried the following RegularizationParameters: 0.01, 0.1, 1, 2.
I also tried different PrecisionGoals: 8, 16, 32, 64.
Any suggestions?

gpt2Model = NetModel["GPT2 Transformer Trained on WebText Data"]
embeddings = 
 NetExtract[gpt2Model, {"embedding", "embeddingtokens", "Weights"}]
{wMat, hMat} = 
  ResourceFunction["NonNegativeMatrixFactorization"][
   Normal[embeddings], 256, "RegularizationParameter" -> 2, 
   MaxSteps -> 300, PrecisionGoal -> 64];

Screenshot 2024-02-29 at 10 17 12โ€ฏAM

Better to change .m to .wl ?

Hi @antononcube . Thank you for this awesome project, and I have spent serveral days reading your documents.
I do recommend to rename all the .m file to .wl, so it won't be messed up with objective-C and MATLAB file.

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