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| </pre><pre class="rust"><code><span class="doccomment">//! Support Vector Machine Module |
| //! |
| //! Contains implementation of Support Vector Machine using the |
| //! [Pegasos training algorithm](http://ttic.uchicago.edu/~nati/Publications/PegasosMPB.pdf). |
| //! |
| //! The SVM models currently only support binary classification. |
| //! The model inputs should be a matrix and the training targets are |
| //! in the form of a vector of `-1`s and `1`s. |
| //! |
| //! # Examples |
| //! |
| //! ``` |
| //! use rusty_machine::learning::svm::SVM; |
| //! use rusty_machine::learning::SupModel; |
| //! |
| //! use rusty_machine::linalg::Matrix; |
| //! use rusty_machine::linalg::Vector; |
| //! |
| //! let inputs = Matrix::new(4,1,vec![1.0,3.0,5.0,7.0]); |
| //! let targets = Vector::new(vec![-1.,-1.,1.,1.]); |
| //! |
| //! let mut svm_mod = SVM::default(); |
| //! |
| //! // Train the model |
| //! svm_mod.train(&inputs, &targets).unwrap(); |
| //! |
| //! // Now we'll predict a new point |
| //! let new_point = Matrix::new(1,1,vec![10.]); |
| //! let output = svm_mod.predict(&new_point).unwrap(); |
| //! |
| //! // Hopefully we classified our new point correctly! |
| //! assert!(output[0] == 1f64, "Our classifier isn't very good!"); |
| //! ``` |
| |
| |
| </span><span class="kw">use </span>linalg::{Matrix, BaseMatrix}; |
| <span class="kw">use </span>linalg::Vector; |
| |
| <span class="kw">use </span>learning::toolkit::kernel::{Kernel, SquaredExp}; |
| <span class="kw">use </span>learning::{LearningResult, SupModel}; |
| <span class="kw">use </span>learning::error::{Error, ErrorKind}; |
| |
| <span class="kw">use </span>rand; |
| <span class="kw">use </span>rand::Rng; |
| |
| <span class="doccomment">/// Support Vector Machine |
| </span><span class="attribute">#[derive(Debug)] |
| </span><span class="kw">pub struct </span>SVM<K: Kernel> { |
| ker: K, |
| alpha: <span class="prelude-ty">Option</span><Vector<f64>>, |
| train_inputs: <span class="prelude-ty">Option</span><Matrix<f64>>, |
| train_targets: <span class="prelude-ty">Option</span><Vector<f64>>, |
| lambda: f64, |
| <span class="doccomment">/// Number of iterations for training. |
| </span><span class="kw">pub </span>optim_iters: usize, |
| } |
| |
| <span class="doccomment">/// The default Support Vector Machine. |
| /// |
| /// The defaults are: |
| /// |
| /// - `ker` = `SquaredExp::default()` |
| /// - `lambda` = `0.3` |
| /// - `optim_iters` = `100` |
| </span><span class="kw">impl </span>Default <span class="kw">for </span>SVM<SquaredExp> { |
| <span class="kw">fn </span>default() -> SVM<SquaredExp> { |
| SVM { |
| ker: SquaredExp::default(), |
| alpha: <span class="prelude-val">None</span>, |
| train_inputs: <span class="prelude-val">None</span>, |
| train_targets: <span class="prelude-val">None</span>, |
| lambda: <span class="number">0.3f64</span>, |
| optim_iters: <span class="number">100</span>, |
| } |
| } |
| } |
| |
| <span class="kw">impl</span><K: Kernel> SVM<K> { |
| <span class="doccomment">/// Constructs an untrained SVM with specified |
| /// kernel and lambda which determins the hardness |
| /// of the margin. |
| /// |
| /// # Examples |
| /// |
| /// ``` |
| /// use rusty_machine::learning::svm::SVM; |
| /// use rusty_machine::learning::toolkit::kernel::SquaredExp; |
| /// |
| /// let _ = SVM::new(SquaredExp::default(), 0.3); |
| /// ``` |
| </span><span class="kw">pub fn </span>new(ker: K, lambda: f64) -> SVM<K> { |
| SVM { |
| ker: ker, |
| alpha: <span class="prelude-val">None</span>, |
| train_inputs: <span class="prelude-val">None</span>, |
| train_targets: <span class="prelude-val">None</span>, |
| lambda: lambda, |
| optim_iters: <span class="number">100</span>, |
| } |
| } |
| } |
| |
| <span class="kw">impl</span><K: Kernel> SVM<K> { |
| <span class="doccomment">/// Construct a kernel matrix |
| </span><span class="kw">fn </span>ker_mat(<span class="kw-2">&</span><span class="self">self</span>, m1: <span class="kw-2">&</span>Matrix<f64>, m2: <span class="kw-2">&</span>Matrix<f64>) -> LearningResult<Matrix<f64>> { |
| <span class="kw">if </span>m1.cols() != m2.cols() { |
| <span class="prelude-val">Err</span>(Error::new(ErrorKind::InvalidState, |
| <span class="string">"Inputs to kernel matrices have different column counts."</span>)) |
| } <span class="kw">else </span>{ |
| <span class="kw">let </span>dim1 = m1.rows(); |
| <span class="kw">let </span>dim2 = m2.rows(); |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>ker_data = Vec::with_capacity(dim1 * dim2); |
| ker_data.extend(m1.row_iter().flat_map(|row1| { |
| m2.row_iter() |
| .map(<span class="kw">move </span>|row2| <span class="self">self</span>.ker.kernel(row1.raw_slice(), row2.raw_slice())) |
| })); |
| |
| <span class="prelude-val">Ok</span>(Matrix::new(dim1, dim2, ker_data)) |
| } |
| } |
| } |
| |
| <span class="doccomment">/// Train the model using the Pegasos algorithm and |
| /// predict the model output from new data. |
| </span><span class="kw">impl</span><K: Kernel> SupModel<Matrix<f64>, Vector<f64>> <span class="kw">for </span>SVM<K> { |
| <span class="kw">fn </span>predict(<span class="kw-2">&</span><span class="self">self</span>, inputs: <span class="kw-2">&</span>Matrix<f64>) -> LearningResult<Vector<f64>> { |
| <span class="kw">let </span>ones = Matrix::<f64>::ones(inputs.rows(), <span class="number">1</span>); |
| <span class="kw">let </span>full_inputs = ones.hcat(inputs); |
| |
| <span class="kw">if let </span>(<span class="kw-2">&</span><span class="prelude-val">Some</span>(<span class="kw-2">ref </span>alpha), <span class="kw-2">&</span><span class="prelude-val">Some</span>(<span class="kw-2">ref </span>train_inputs), <span class="kw-2">&</span><span class="prelude-val">Some</span>(<span class="kw-2">ref </span>train_targets)) = |
| (<span class="kw-2">&</span><span class="self">self</span>.alpha, <span class="kw-2">&</span><span class="self">self</span>.train_inputs, <span class="kw-2">&</span><span class="self">self</span>.train_targets) { |
| <span class="kw">let </span>ker_mat = <span class="self">self</span>.ker_mat(<span class="kw-2">&</span>full_inputs, train_inputs)<span class="question-mark">?</span>; |
| <span class="kw">let </span>weight_vec = alpha.elemul(train_targets) / <span class="self">self</span>.lambda; |
| |
| <span class="kw">let </span>plane_dist = ker_mat * weight_vec; |
| |
| <span class="prelude-val">Ok</span>(plane_dist.apply(<span class="kw-2">&</span>|d| d.signum())) |
| } <span class="kw">else </span>{ |
| <span class="prelude-val">Err</span>(Error::new_untrained()) |
| } |
| } |
| |
| <span class="kw">fn </span>train(<span class="kw-2">&mut </span><span class="self">self</span>, inputs: <span class="kw-2">&</span>Matrix<f64>, targets: <span class="kw-2">&</span>Vector<f64>) -> LearningResult<()> { |
| <span class="kw">let </span>n = inputs.rows(); |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>rng = rand::thread_rng(); |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>alpha = <span class="macro">vec!</span>[<span class="number">0f64</span>; n]; |
| |
| <span class="kw">let </span>ones = Matrix::<f64>::ones(inputs.rows(), <span class="number">1</span>); |
| <span class="kw">let </span>full_inputs = ones.hcat(inputs); |
| |
| <span class="kw">for </span>t <span class="kw">in </span><span class="number">0</span>..<span class="self">self</span>.optim_iters { |
| <span class="kw">let </span>i = rng.gen_range(<span class="number">0</span>..n); |
| <span class="kw">let </span>row_i = full_inputs.select_rows(<span class="kw-2">&</span>[i]); |
| <span class="kw">let </span>sum = full_inputs.row_iter() |
| .fold(<span class="number">0f64</span>, |sum, row| sum + <span class="self">self</span>.ker.kernel(row_i.data(), row.raw_slice())) * |
| targets[i] / (<span class="self">self</span>.lambda * (t <span class="kw">as </span>f64)); |
| |
| <span class="kw">if </span>sum < <span class="number">1f64 </span>{ |
| alpha[i] += <span class="number">1f64</span>; |
| } |
| } |
| |
| <span class="self">self</span>.alpha = <span class="prelude-val">Some</span>(Vector::new(alpha) / (<span class="self">self</span>.optim_iters <span class="kw">as </span>f64)); |
| <span class="self">self</span>.train_inputs = <span class="prelude-val">Some</span>(full_inputs); |
| <span class="self">self</span>.train_targets = <span class="prelude-val">Some</span>(targets.clone()); |
| |
| <span class="prelude-val">Ok</span>(()) |
| } |
| } |
| </code></pre></div> |
| </section></div></main><div id="rustdoc-vars" data-root-path="../../../" data-current-crate="rusty_machine" data-themes="ayu,dark,light" data-resource-suffix="" data-rustdoc-version="1.66.0-nightly (5c8bff74b 2022-10-21)" ></div></body></html> |
