Struct rusty_machine::learning::lin_reg::LinRegressor
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pub struct LinRegressor { /* fields omitted */ }
Linear Regression Model.
Contains option for optimized parameter.
Methods
impl LinRegressor
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fn parameters(&self) -> Option<&Vector<f64>>
Get the parameters from the model.
Returns an option that is None if the model has not been trained.
impl LinRegressor
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fn train_with_optimization(&mut self,
inputs: &Matrix<f64>,
targets: &Vector<f64>)
inputs: &Matrix<f64>,
targets: &Vector<f64>)
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();
Trait Implementations
impl Debug for LinRegressor
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impl Default for LinRegressor
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fn default() -> LinRegressor
Returns the "default value" for a type. Read more
impl SupModel<Matrix<f64>, Vector<f64>> for LinRegressor
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fn train(&mut self,
inputs: &Matrix<f64>,
targets: &Vector<f64>)
-> LearningResult<()>
inputs: &Matrix<f64>,
targets: &Vector<f64>)
-> LearningResult<()>
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();
fn predict(&self, inputs: &Matrix<f64>) -> LearningResult<Vector<f64>>
Predict output value from input data.
Model must be trained before prediction can be made.