Struct smartcore::svm::svr::SVR

pub struct SVR<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> { /* fields omitted */ }

Epsilon-Support Vector Regression

Implementations

impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> SVR<T, M, K>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: SVRParameters<T, M, K>
) -> Result<SVR<T, M, K>, 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: &M) -> Result<M::RowVector, Failed>

Predict target values from x

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

Trait Implementations

impl<T: Debug + RealNumber, M: Debug + Matrix<T>, K: Debug + Kernel<T, M::RowVector>> Debug for SVR<T, M, K> where
    M::RowVector: Debug, 

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

Formats the value using the given formatter.

impl<'de, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Deserialize<'de> for SVR<T, M, K> where
    M::RowVector: Deserialize<'de>,
    K: Deserialize<'de>,
    T: Deserialize<'de>, 

pub fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.

impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> PartialEq<SVR<T, M, K>> for SVR<T, M, K>

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

This method tests for self and other values to be equal, and is used by ==.

#[must_use]pub fn ne(&self, other: &Rhs) -> bool

This method tests for !=.

impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Predictor<M, <M as BaseMatrix<T>>::RowVector> for SVR<T, M, K>

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

Estimate target values from new data.

impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Serialize for SVR<T, M, K> where
    M::RowVector: Serialize,
    K: Serialize,
    T: Serialize, 

pub fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, SVRParameters<T, M, K>> for SVR<T, M, K>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: SVRParameters<T, M, K>
) -> Result<Self, Failed>

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