automl 0.3.0

Automated machine learning for classification and regression
Documentation 
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//! Support Vector Regression parameters

pub use crate::utils::kernels::Kernel;

/// Parameters for support vector regression
#[derive(serde::Serialize, serde::Deserialize)]
pub struct SVRParameters {
    /// Epsilon in the epsilon-SVR model.
    pub(crate) eps: f32,
    /// Regularization parameter.
    pub(crate) c: f32,
    /// Tolerance for stopping criterion.
    pub(crate) tol: f32,
    /// Kernel to use for the SVR model
    pub(crate) kernel: Kernel,
}

impl SVRParameters {
    /// Define the value of epsilon to use in the epsilon-SVR model.
    #[must_use]
    pub const fn with_eps(mut self, eps: f32) -> Self {
        self.eps = eps;
        self
    }

    /// Define the regulation penalty to use with the SVR Model
    #[must_use]
    pub const fn with_c(mut self, c: f32) -> Self {
        self.c = c;
        self
    }

    /// Define the convergence tolerance to use with the SVR model
    #[must_use]
    pub const fn with_tol(mut self, tol: f32) -> Self {
        self.tol = tol;
        self
    }

    /// Define which kernel to use with the SVR model
    #[must_use]
    pub const fn with_kernel(mut self, kernel: Kernel) -> Self {
        self.kernel = kernel;
        self
    }
}

impl Default for SVRParameters {
    fn default() -> Self {
        Self {
            eps: 0.1,
            c: 1.0,
            tol: 1e-3,
            kernel: Kernel::Linear,
        }
    }
}