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* Bump version 0.4.0, add release notes * Add t-SNE example to release notes * Add example for t-SNE and start writing preprocessing example * Fix typo * Add example for TF-IDF text preprocessing * Explain that target construction is omitted and traits changes * Add contribute section on the new lapack traits * Link to crates.io in the base crate documentation * Link to crates.io in the base crate documentation * Add mnist example * Run rustfmt * Fix mnist example
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# Partial Least Squares | ||
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`linfa-pls` provides a pure Rust implementation of the partial least squares algorithm family. | ||
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## The Big Picture | ||
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`linfa-pls` is a crate in the [`linfa`](https://crates.io/crates/linfa) ecosystem, an effort to create a toolkit for classical Machine Learning implemented in pure Rust, akin to Python's `scikit-learn`. | ||
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## Current state | ||
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`linfa-pls` currently provides an implementation of the following methods: | ||
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- Partial Least Squares | ||
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## Examples | ||
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There is an usage example in the `examples/` directory. The example uses a BLAS backend, to run it and use the `intel-mkl` library do: | ||
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```bash | ||
$ cargo run --example pls_regression --features linfa/intel-mkl-system | ||
``` | ||
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## License | ||
Dual-licensed to be compatible with the Rust project. | ||
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Licensed under the Apache License, Version 2.0 <http://www.apache.org/licenses/LICENSE-2.0> or the MIT license <http://opensource.org/licenses/MIT>, at your option. This file may not be copied, modified, or distributed except according to those terms. | ||
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