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| </pre><pre class="rust"><code><span class="doccomment">//! Linear Regression module |
| //! |
| //! Contains implemention of linear regression using |
| //! OLS and gradient descent optimization. |
| //! |
| //! The regressor will automatically add the intercept term |
| //! so you do not need to format the input matrices yourself. |
| //! |
| //! # Usage |
| //! |
| //! ``` |
| //! use rusty_machine::learning::lin_reg::LinRegressor; |
| //! 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.,5.,9.,13.]); |
| //! |
| //! let mut lin_mod = LinRegressor::default(); |
| //! |
| //! // Train the model |
| //! lin_mod.train(&inputs, &targets).unwrap(); |
| //! |
| //! // Now we'll predict a new point |
| //! let new_point = Matrix::new(1,1,vec![10.]); |
| //! let output = lin_mod.predict(&new_point).unwrap(); |
| //! |
| //! // Hopefully we classified our new point correctly! |
| //! assert!(output[0] > 17f64, "Our regressor 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::{LearningResult, SupModel}; |
| <span class="kw">use </span>learning::toolkit::cost_fn::CostFunc; |
| <span class="kw">use </span>learning::toolkit::cost_fn::MeanSqError; |
| <span class="kw">use </span>learning::optim::grad_desc::GradientDesc; |
| <span class="kw">use </span>learning::optim::{OptimAlgorithm, Optimizable}; |
| <span class="kw">use </span>learning::error::Error; |
| |
| <span class="doccomment">/// Linear Regression Model. |
| /// |
| /// Contains option for optimized parameter. |
| </span><span class="attribute">#[derive(Debug)] |
| </span><span class="kw">pub struct </span>LinRegressor { |
| <span class="doccomment">/// The parameters for the regression model. |
| </span>parameters: <span class="prelude-ty">Option</span><Vector<f64>>, |
| } |
| |
| <span class="kw">impl </span>Default <span class="kw">for </span>LinRegressor { |
| <span class="kw">fn </span>default() -> LinRegressor { |
| LinRegressor { parameters: <span class="prelude-val">None </span>} |
| } |
| } |
| |
| <span class="kw">impl </span>LinRegressor { |
| <span class="doccomment">/// Get the parameters from the model. |
| /// |
| /// Returns an option that is None if the model has not been trained. |
| </span><span class="kw">pub fn </span>parameters(<span class="kw-2">&</span><span class="self">self</span>) -> <span class="prelude-ty">Option</span><<span class="kw-2">&</span>Vector<f64>> { |
| <span class="self">self</span>.parameters.as_ref() |
| } |
| } |
| |
| <span class="kw">impl </span>SupModel<Matrix<f64>, Vector<f64>> <span class="kw">for </span>LinRegressor { |
| <span class="doccomment">/// Train the linear regression model. |
| /// |
| /// Takes training data and output values as input. |
| /// |
| /// # Examples |
| /// |
| /// ``` |
| /// use rusty_machine::learning::lin_reg::LinRegressor; |
| /// use rusty_machine::linalg::Matrix; |
| /// use rusty_machine::linalg::Vector; |
| /// use rusty_machine::learning::SupModel; |
| /// |
| /// let mut lin_mod = LinRegressor::default(); |
| /// let inputs = Matrix::new(3,1, vec![2.0, 3.0, 4.0]); |
| /// let targets = Vector::new(vec![5.0, 6.0, 7.0]); |
| /// |
| /// lin_mod.train(&inputs, &targets).unwrap(); |
| /// ``` |
| </span><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>ones = Matrix::<f64>::ones(inputs.rows(), <span class="number">1</span>); |
| <span class="kw">let </span>full_inputs = ones.hcat(inputs); |
| |
| <span class="kw">let </span>xt = full_inputs.transpose(); |
| <span class="self">self</span>.parameters = <span class="prelude-val">Some</span>((<span class="kw-2">&</span>xt * full_inputs).solve(<span class="kw-2">&</span>xt * targets)<span class="question-mark">?</span>); |
| <span class="prelude-val">Ok</span>(()) |
| } |
| |
| <span class="doccomment">/// Predict output value from input data. |
| /// |
| /// Model must be trained before prediction can be made. |
| </span><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">if let </span><span class="prelude-val">Some</span>(<span class="kw-2">ref </span>v) = <span class="self">self</span>.parameters { |
| <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="prelude-val">Ok</span>(full_inputs * v) |
| } <span class="kw">else </span>{ |
| <span class="prelude-val">Err</span>(Error::new_untrained()) |
| } |
| } |
| } |
| |
| <span class="kw">impl </span>Optimizable <span class="kw">for </span>LinRegressor { |
| <span class="kw">type </span>Inputs = Matrix<f64>; |
| <span class="kw">type </span>Targets = Vector<f64>; |
| |
| <span class="kw">fn </span>compute_grad(<span class="kw-2">&</span><span class="self">self</span>, |
| params: <span class="kw-2">&</span>[f64], |
| inputs: <span class="kw-2">&</span>Matrix<f64>, |
| targets: <span class="kw-2">&</span>Vector<f64>) |
| -> (f64, Vec<f64>) { |
| |
| <span class="kw">let </span>beta_vec = Vector::new(params.to_vec()); |
| <span class="kw">let </span>outputs = inputs * beta_vec; |
| |
| <span class="kw">let </span>cost = MeanSqError::cost(<span class="kw-2">&</span>outputs, targets); |
| <span class="kw">let </span>grad = (inputs.transpose() * (outputs - targets)) / (inputs.rows() <span class="kw">as </span>f64); |
| |
| (cost, grad.into_vec()) |
| } |
| } |
| |
| <span class="kw">impl </span>LinRegressor { |
| <span class="doccomment">/// Train the linear regressor using Gradient Descent. |
| /// |
| /// # Examples |
| /// |
| /// ``` |
| /// use rusty_machine::learning::lin_reg::LinRegressor; |
| /// 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.,5.,9.,13.]); |
| /// |
| /// let mut lin_mod = LinRegressor::default(); |
| /// |
| /// // Train the model |
| /// lin_mod.train_with_optimization(&inputs, &targets); |
| /// |
| /// // Now we'll predict a new point |
| /// let new_point = Matrix::new(1,1,vec![10.]); |
| /// let _ = lin_mod.predict(&new_point).unwrap(); |
| /// ``` |
| </span><span class="kw">pub fn </span>train_with_optimization(<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>) { |
| <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">let </span>initial_params = <span class="macro">vec!</span>[<span class="number">0.</span>; full_inputs.cols()]; |
| |
| <span class="kw">let </span>gd = GradientDesc::default(); |
| <span class="kw">let </span>optimal_w = gd.optimize(<span class="self">self</span>, <span class="kw-2">&</span>initial_params[..], <span class="kw-2">&</span>full_inputs, targets); |
| <span class="self">self</span>.parameters = <span class="prelude-val">Some</span>(Vector::new(optimal_w)); |
| } |
| } |
| </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> |