Struct smartcore::linear::linear_regression::LinearRegression

pub struct LinearRegression<T: RealNumber, M: Matrix<T>> { /* fields omitted */ }

Linear Regression

Implementations

impl<T: RealNumber, M: Matrix<T>> LinearRegression<T, M>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: LinearRegressionParameters
) -> Result<LinearRegression<T, M>, Failed>

Fits Linear Regression to your data.

• x - NxM matrix with N observations and M features in each observation.
• y - target values
• parameters - other 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.

pub fn coefficients(&self) -> &M

Get estimates regression coefficients

pub fn intercept(&self) -> T

Get estimate of intercept

Trait Implementations

impl<T: Debug + RealNumber, M: Debug + Matrix<T>> Debug for LinearRegression<T, M>

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

Formats the value using the given formatter.

impl<'de, T: RealNumber, M: Matrix<T>> Deserialize<'de> for LinearRegression<T, M> where
    T: Deserialize<'de>,
    M: 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>> PartialEq<LinearRegression<T, M>> for LinearRegression<T, M>

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>> Predictor<M, <M as BaseMatrix<T>>::RowVector> for LinearRegression<T, M>

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

Estimate target values from new data.

impl<T: RealNumber, M: Matrix<T>> Serialize for LinearRegression<T, M> where
    T: Serialize,
    M: 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>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, LinearRegressionParameters> for LinearRegression<T, M>

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

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