fix(deps): update dependency com.github.haifengl:smile-core to v3

[renovate]

This PR contains the following updates:

Package	Change	Age	Adoption	Passing	Confidence
com.github.haifengl:smile-core	1.5.1 -> 3.0.2

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Warning

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Release Notes

haifengl/smile (com.github.haifengl:smile-core)

v3.0.2: 3.0.2

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

v3.0.1: 3.0.1

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

v3.0.0: 3.0.0

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1. Java Module friendly with auto module name
2. Redesigned feature engineering packages (missing value imputation, transform, selection, extraction, importance)
3. One-class SVM
4. Isolation forest
5. Feature Hashing
6. One-way ANOVA
7. BigMatrix supporting more than 2 billion elements
8. Latin hypercube sampling
9. CLI supports training, batch prediction, endpoint, etc.
10. Bug fixes

v2.6.0: 2.6.0

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• 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

v2.5.3: 2.5.3

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1. enhance MLP
2. bug fixes.

v2.5.2: 2.5.2

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

v2.5.1: 2.5.1

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• 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.

v2.5.0: 2.5.0

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1. New matrix design
2. New formula design
3. Generalized linear models (GLM)
4. Sparse logistic regression
5. Bug fixes

v2.4.0: 2.4.0

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• All new declarative data visualization
• TreeSHAP (contributed by Ray Ma @​rayeaster)
• UMAP (contributed by Ray Ma @​rayeaster)
• Levenberg-Marquardt algorithm
• The packages smile-cas and smile-vega are merged into scala-scala package
• Spark integration in smile-spark
• NLP in Kotlin
• Grid search and random search for hyperparameter tuning
• Bug fixes
• Smile Shell is based on Scala REPL (2.13.2) again
• DataFrame and Tuple -> JSON
• Kotlin and Clojure notebooks

Kudos to Ray Ma @​rayeaster for great contributions!

v2.3.0: 2.3.0

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• Kotlin API
• Clojure API
• smile.plot.swing API is redesigned. Leaner, simpler, and better headless support
• Bug fixes

v2.2.2: 2.2.2

Various minor improvements

v2.2.0: 2.2.0

The CAS module is a computer algebra system that has the ability to manipulate mathematical
expressions in a way similar to the traditional manual computations of
mathematicians and scientists.

The symbolic manipulations supported include:

• simplification to a smaller expression or some standard form,
  including automatic simplification with assumptions and
  simplification with constraints

• substitution of symbols or numeric values for certain expressions

• change of form of expressions: expanding products and powers, partial
  and full factorization, rewriting as partial fractions, constraint
  satisfaction, rewriting trigonometric functions as exponentials,
  transforming logic expressions, etc.

• partial and total differentiation

• matrix operations including products, inverses, etc.

v2.1.0: 2.1.0

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1. Vega-lite based plot
2. Jupyter notebook examples
3. Bug fixes

v2.0.0: 2.0.0

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Smile has been fully rewritten with more than 150,000 lines change.

• Fully redesigned API. It is leaner, simpler and even more friendly.
• Faster implementation and memory optimization. Many algorithms are fully reimplemented. RandomForest is 8X faster than XGBoost on large benchmark data (10MM samples).
• New parallelism mechanism
• All new DataFrame and Formula
• New algorithms such as ICA, error reduction prune, quantile loss, TWCNB, etc.
• Support arbitrary class labels.
• Enhancement and harden numeric computations.
• Support Parquet, SAS, Arrow, Avro, etc.
• Bug fixes.

v1.5.3: 1.5.3

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• ElasticNet
• GroupKFold
• Bug fixes

v1.5.2: 1.5.2

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• K-Modes clustering
• Online learning with LogisticRegression by SGD
• MCC (Matthews correlation coefficient) metric
• Bug fixes

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