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77 lines
2.0 KiB
Markdown
77 lines
2.0 KiB
Markdown
# Machine Learning
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The `compute::models` module bundles several learning algorithms that operate on
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`Matrix` structures. These examples highlight the basic training and prediction
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APIs. For more end‑to‑end walkthroughs see the examples directory in the
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repository.
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Currently implemented models include:
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- Linear and logistic regression
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- K‑means clustering
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- Principal component analysis (PCA)
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- Gaussian Naive Bayes
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- Dense neural networks
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## Linear Regression
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```rust
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# extern crate rustframe;
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use rustframe::compute::models::linreg::LinReg;
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use rustframe::matrix::Matrix;
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let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
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let y = Matrix::from_vec(vec![2.0, 3.0, 4.0, 5.0], 4, 1);
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let mut model = LinReg::new(1);
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model.fit(&x, &y, 0.01, 100);
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let preds = model.predict(&x);
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assert_eq!(preds.rows(), 4);
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```
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## K-means Walkthrough
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```rust
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# extern crate rustframe;
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use rustframe::compute::models::k_means::KMeans;
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use rustframe::matrix::Matrix;
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let data = Matrix::from_vec(vec![1.0, 1.0, 5.0, 5.0], 2, 2);
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let (model, _labels) = KMeans::fit(&data, 2, 10, 1e-4);
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let new_point = Matrix::from_vec(vec![0.0, 0.0], 1, 2);
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let cluster = model.predict(&new_point)[0];
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```
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For helper functions and upcoming modules, visit the
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[utilities](./utilities.md) section.
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## Logistic Regression
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```rust
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# extern crate rustframe;
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use rustframe::compute::models::logreg::LogReg;
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use rustframe::matrix::Matrix;
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let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
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let y = Matrix::from_vec(vec![0.0, 0.0, 1.0, 1.0], 4, 1);
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let mut model = LogReg::new(1);
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model.fit(&x, &y, 0.1, 200);
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let preds = model.predict_proba(&x);
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assert_eq!(preds.rows(), 4);
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```
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## Principal Component Analysis
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```rust
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# extern crate rustframe;
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use rustframe::compute::models::pca::PCA;
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use rustframe::matrix::Matrix;
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let data = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
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let pca = PCA::fit(&data, 1, 0);
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let transformed = pca.transform(&data);
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assert_eq!(transformed.cols(), 1);
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```
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For helper functions and upcoming modules, visit the
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[utilities](./utilities.md) section.
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