Bump com.github.haifengl:smile-core from 1.5.2 to 3.1.0

[dependabot[bot]]

Bumps com.github.haifengl:smile-core from 1.5.2 to 3.1.0.

Release notes

Sourced from com.github.haifengl:smile-core's releases.

3.1.0

• Declarative Data Visualization for Java

3.0.3

Bug fixes.

3.0.2

1. Minor bug fixes.
2. Improve flaky tests.

3.0.1

1. Remove XStream dependency as it exposes many vulnerabilities
2. Bug fixes with ICA, MCC, Shap Value, etc.

3.0.0

1. Switch to dual license model to meet open-source projects (GPL) and the development and distribution needs of commercial distributors (such as OEMs, ISVs and VARs).
2. Java Module friendly with auto module name
3. Redesigned feature engineering packages (missing value imputation, transform, selection, extraction, importance)
4. One-class SVM
5. Isolation forest
6. Feature Hashing
7. One-way ANOVA
8. BigMatrix supporting more than 2 billion elements
9. Latin hypercube sampling
10. CLI supports training, batch prediction, endpoint, etc.
11. Bug fixes

2.6.0

• Spark integration (thanks Pierre Nodet)
• t-SNE is 6X faster (thanks Brault Olivier-O)
• Fully redesigned Gaussian Process Regression with HPO
• L-BFGS-B
• Matern kernel and composed kernels
• Fully redesigned model validation facilities and metrics
• Various optimization and bug fixes

2.5.3

1. enhance MLP
2. bug fixes.

2.5.2

1. AR and ARMA for time series modeling
2. Optimize interpolation package
3. Optimize matrix decomposition memory usage.
4. Bug fixes.

2.5.1

• Generalized ridge regression
• Enhance JSON library
• Do NOT transparently include OpenBLAS library to save space. If the users need optimized matrix computation, they should add the dependency based on their platform. See README for details.

... (truncated)

Commits

• 39ede9b v3.1.0
• 89a43ed fix javadoc
• 6ef9c63 implement Layer interface
• 4363b82 add sigmoid/tanh/shrink activation
• 673e54f 10 epochs
• 99704c1 free up device memory after each epoch
• 5f9ac52 add emptyCache() method
• ae5aed8 add deallocate() method
• 03da46b add isCUDA/CPU/MPS methods
• c11be36 assertEquals on Accuracy vs Micro-Precision/Recall and Weighted-Recall
• Additional commits viewable in compare view

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Superseded by #406.