Struct rusty_machine::learning::logistic_reg::LogisticRegressor

pub struct LogisticRegressor<A> where A: OptimAlgorithm<BaseLogisticRegressor> { /* fields omitted */ }

Logistic Regression Model.

Contains option for optimized parameter.

Methods

impl<A: OptimAlgorithm<BaseLogisticRegressor>> LogisticRegressor<A>

fn new(alg: A) -> LogisticRegressor<A>

Constructs untrained logistic regression model.

Examples

use rusty_machine::learning::logistic_reg::LogisticRegressor;
use rusty_machine::learning::optim::grad_desc::GradientDesc;

let gd = GradientDesc::default();
let mut logistic_mod = LogisticRegressor::new(gd);

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.

Trait Implementations

impl<A: Debug> Debug for LogisticRegressor<A> where A: OptimAlgorithm<BaseLogisticRegressor>

fn fmt(&self, __arg_0: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Default for LogisticRegressor<GradientDesc>

Constructs a default Logistic Regression model using standard gradient descent.

fn default() -> LogisticRegressor<GradientDesc>

Returns the "default value" for a type.

impl<A> SupModel<Matrix<f64>, Vector<f64>> for LogisticRegressor<A> where A: OptimAlgorithm<BaseLogisticRegressor>

fn train(&mut self,
         inputs: &Matrix<f64>,
         targets: &Vector<f64>)
         -> LearningResult<()>

Train the logistic regression model.

Takes training data and output values as input.

Examples

use rusty_machine::learning::logistic_reg::LogisticRegressor;
use rusty_machine::linalg::Matrix;
use rusty_machine::linalg::Vector;
use rusty_machine::learning::SupModel;

let mut logistic_mod = LogisticRegressor::default();
let inputs = Matrix::new(3,2, vec![1.0, 2.0, 1.0, 3.0, 1.0, 4.0]);
let targets = Vector::new(vec![5.0, 6.0, 7.0]);

logistic_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.