Struct SVR 

pub struct SVR<'a, T: Number + FloatNumber + PartialOrd, X: Array2<T>, Y: Array1<T>> { /* private fields */ }

Epsilon-Support Vector Regression

Implementations

impl<'a, T: Number + FloatNumber + PartialOrd, X: Array2<T>, Y: Array1<T>> SVR<'a, T, X, Y>

pub fn fit( x: &'a X, y: &'a Y, parameters: &'a SVRParameters<T>, ) -> Result<SVR<'a, T, X, Y>, Failed>

Fits SVR to your data.

• x - NxM matrix with N observations and M features in each observation.
• y - target values
• kernel - the kernel function
• parameters - optional parameters, use Default::default() to set parameters to default values.

pub fn predict(&self, x: &'a X) -> Result<Vec<T>, Failed>

Predict target values from x

• x - KxM data where K is number of observations and M is number of features.

Trait Implementations

impl<'a, T: Debug + Number + FloatNumber + PartialOrd, X: Debug + Array2<T>, Y: Debug + Array1<T>> Debug for SVR<'a, T, X, Y>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'a, T: Number + FloatNumber + PartialOrd, X: Array2<T>, Y: Array1<T>> PartialEq for SVR<'a, T, X, Y>

fn eq(&self, other: &Self) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<'a, T: Number + FloatNumber + PartialOrd, X: Array2<T>, Y: Array1<T>> PredictorBorrow<'a, X, T> for SVR<'a, T, X, Y>

fn predict(&self, x: &'a X) -> Result<Vec<T>, Failed>

Estimate target values from new data.

impl<'a, T: Number + FloatNumber + PartialOrd, X: Array2<T>, Y: Array1<T>> SupervisedEstimatorBorrow<'a, X, Y, SVRParameters<T>> for SVR<'a, T, X, Y>

fn new() -> Self

Empty constructor, instantiate an empty estimator. Object is dropped as soon as fit() is called. used to pass around the correct fit() implementation. by calling ::fit(). mostly used to be used with model_selection::cross_validate(...)

fn fit( x: &'a X, y: &'a Y, parameters: &'a SVRParameters<T>, ) -> Result<Self, Failed>

Fit a model to a training dataset, estimate model’s parameters.