Enum GpError 

pub enum GpError {
    LikelihoodComputationError(String),
    LinalgError(LinalgError),
    EmptyCluster(String),
    PlsError(PlsError),
    LinfaError(Error),
    LoadIoError(Error),
    LoadError(String),
    InvalidValueError(String),
}

An error when using GaussianProcess or a SparseGaussianProcess algorithm

Variants

LikelihoodComputationError(String)

When LikelihoodComputation computation fails

LinalgError(LinalgError)

When linear algebra computation fails

EmptyCluster(String)

When clustering fails

PlsError(PlsError)

When PLS fails

LinfaError(Error)

When a linfa error occurs

LoadIoError(Error)

When error during loading

LoadError(String)

When error during loading

InvalidValueError(String)

When error dur to a bad value

Trait Implementations

impl Debug for GpError

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

Formats the value using the given formatter.

impl Display for GpError

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

Formats the value using the given formatter.

impl Error for GpError

fn source(&self) -> Option<&(dyn Error + 'static)>

Returns the lower-level source of this error, if any.

fn description(&self) -> &str

👎Deprecated since 1.42.0:

use the Display impl or to_string()

fn cause(&self) -> Option<&dyn Error>

👎Deprecated since 1.33.0:

replaced by Error::source, which can support downcasting

fn provide<'a>(&'a self, request: &mut Request<'a>)

🔬This is a nightly-only experimental API. (error_generic_member_access)

Provides type-based access to context intended for error reports.

impl<F: Float, Mean: RegressionModel<F>, Corr: CorrelationModel<F>, D: Data<Elem = F>> Fit<ArrayBase<D, Dim<[usize; 2]>>, ArrayBase<D, Dim<[usize; 1]>>, GpError> for GpValidParams<F, Mean, Corr>

fn fit( &self, dataset: &DatasetBase<ArrayBase<D, Ix2>, ArrayBase<D, Ix1>>, ) -> Result<Self::Object>

Fit GP parameters using maximum likelihood

type Object = GaussianProcess<F, Mean, Corr>

impl<F: Float, Corr: CorrelationModel<F>, D: Data<Elem = F> + Sync> Fit<ArrayBase<D, Dim<[usize; 2]>>, ArrayBase<D, Dim<[usize; 1]>>, GpError> for SgpValidParams<F, Corr>

fn fit( &self, dataset: &DatasetBase<ArrayBase<D, Ix2>, ArrayBase<D, Ix1>>, ) -> Result<Self::Object>

Fit GP parameters using maximum likelihood

type Object = SparseGaussianProcess<F, Corr>

impl From<Error> for GpError

fn from(source: Error) -> Self

Converts to this type from the input type.

impl From<Error> for GpError

fn from(source: Error) -> Self

Converts to this type from the input type.

impl From<LinalgError> for GpError

fn from(source: LinalgError) -> Self

Converts to this type from the input type.

impl From<PlsError> for GpError

fn from(source: PlsError) -> Self

Converts to this type from the input type.

impl<F, Mean, Corr> PredictScore<F, GpError, GpParams<F, Mean, Corr>, GaussianProcess<F, Mean, Corr>> for GaussianProcess<F, Mean, Corr>

where F: Float, Mean: RegressionModel<F>, Corr: CorrelationModel<F>,

fn training_data(&self) -> &(Array2<F>, Array1<F>)

Return the training data (xt, yt)

fn params(&self) -> GpParams<F, Mean, Corr>

Return the model parameters

fn q2_score(&self, kfold: usize) -> F

Compute quality metric Q2 with kfold cross validation

fn looq2_score(&self) -> F

Q2 predictive coefficient with Leave-One-Out Cross-Validation

impl<F, Corr> PredictScore<F, GpError, SgpParams<F, Corr>, SparseGaussianProcess<F, Corr>> for SparseGaussianProcess<F, Corr>

where F: Float, Corr: CorrelationModel<F>,

fn training_data(&self) -> &(Array2<F>, Array1<F>)

Return the training data (xt, yt)

fn params(&self) -> SgpParams<F, Corr>

Return the model parameters

fn q2_score(&self, kfold: usize) -> F

Compute quality metric Q2 with kfold cross validation

fn looq2_score(&self) -> F

Q2 predictive coefficient with Leave-One-Out Cross-Validation