Struct SVM 

pub struct SVM {
    pub loss_function: LossFunctionType,
    pub activation_function: ActivationFunctionType,
    pub kernel_function: KernelFunctionType,
    pub intercept: f64,
    pub coefficients: Vec<f64>,
}

A struct that defines a support vector machine.

Example

let model = Model::SVM::new();

model.fit(&x, &y, num_epochs, learning_rate);

let y_pred = model.predict(&x);

Fields

loss_function: LossFunctionType

activation_function: ActivationFunctionType

kernel_function: KernelFunctionType

intercept: f64

coefficients: Vec<f64>

Implementations

impl SVM

pub fn new() -> SVM

Create a new SVM model.

Example

let model = Model::SVM::new();

Trait Implementations

impl SupervisedModeller for SVM

fn fit( &mut self, x: &DataFrame, y: &Series, num_epochs: u32, learning_rate: f64, ) -> Result<(), PolarsError>

Fit the model to the data.

Example

let model = SVM::new();

model.fit(&x, &y, num_epochs, learning_rate);

fn predict(&self, x: &DataFrame) -> Result<Series, PolarsError>

Predict the output of the model.

Arguments

• x - The DataFrame of input data.

Returns

The Series of predictions.

Example

 
let predictions = model.predict(&x);
 

fn accuracy(&self, x: &DataFrame, y: &Series) -> Result<f64, PolarsError>

Compute the accuracy of the model.

fn loss(&self, x: &DataFrame, y: &Series) -> Result<f64, PolarsError>

Compute the loss of the model using its stored loss function.