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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(&amp;inputs, &amp;targets).unwrap();
//!
//! // Now we&#39;ll predict a new point
//! let new_point = Matrix::new(1,1,vec![10.]);
//! let output = lin_mod.predict(&amp;new_point).unwrap();
//!
//! // Hopefully we classified our new point correctly!
//! assert!(output[0] &gt; 17f64, &quot;Our regressor isn&#39;t very good!&quot;);
//! ```
</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>&lt;Vector&lt;f64&gt;&gt;,
}
<span class="kw">impl </span>Default <span class="kw">for </span>LinRegressor {
<span class="kw">fn </span>default() -&gt; 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">&amp;</span><span class="self">self</span>) -&gt; <span class="prelude-ty">Option</span>&lt;<span class="kw-2">&amp;</span>Vector&lt;f64&gt;&gt; {
<span class="self">self</span>.parameters.as_ref()
}
}
<span class="kw">impl </span>SupModel&lt;Matrix&lt;f64&gt;, Vector&lt;f64&gt;&gt; <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(&amp;inputs, &amp;targets).unwrap();
/// ```
</span><span class="kw">fn </span>train(<span class="kw-2">&amp;mut </span><span class="self">self</span>, inputs: <span class="kw-2">&amp;</span>Matrix&lt;f64&gt;, targets: <span class="kw-2">&amp;</span>Vector&lt;f64&gt;) -&gt; LearningResult&lt;()&gt; {
<span class="kw">let </span>ones = Matrix::&lt;f64&gt;::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">&amp;</span>xt * full_inputs).solve(<span class="kw-2">&amp;</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">&amp;</span><span class="self">self</span>, inputs: <span class="kw-2">&amp;</span>Matrix&lt;f64&gt;) -&gt; LearningResult&lt;Vector&lt;f64&gt;&gt; {
<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::&lt;f64&gt;::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&lt;f64&gt;;
<span class="kw">type </span>Targets = Vector&lt;f64&gt;;
<span class="kw">fn </span>compute_grad(<span class="kw-2">&amp;</span><span class="self">self</span>,
params: <span class="kw-2">&amp;</span>[f64],
inputs: <span class="kw-2">&amp;</span>Matrix&lt;f64&gt;,
targets: <span class="kw-2">&amp;</span>Vector&lt;f64&gt;)
-&gt; (f64, Vec&lt;f64&gt;) {
<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">&amp;</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(&amp;inputs, &amp;targets);
///
/// // Now we&#39;ll predict a new point
/// let new_point = Matrix::new(1,1,vec![10.]);
/// let _ = lin_mod.predict(&amp;new_point).unwrap();
/// ```
</span><span class="kw">pub fn </span>train_with_optimization(<span class="kw-2">&amp;mut </span><span class="self">self</span>, inputs: <span class="kw-2">&amp;</span>Matrix&lt;f64&gt;, targets: <span class="kw-2">&amp;</span>Vector&lt;f64&gt;) {
<span class="kw">let </span>ones = Matrix::&lt;f64&gt;::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">&amp;</span>initial_params[..], <span class="kw-2">&amp;</span>full_inputs, targets);
<span class="self">self</span>.parameters = <span class="prelude-val">Some</span>(Vector::new(optimal_w));
}
}
</code></pre></div>
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