Struct linfa_pls::PlsRegressionParams [−][src]
pub struct PlsRegressionParams<F: Float>(_);
Implementations
impl<F: Float> PlsRegressionParams<F>
[src]
impl<F: Float> PlsRegressionParams<F>
[src]pub fn max_iterations(self, max_iter: usize) -> Self
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Set the maximum number of iterations of the power method when algorithm=‘Nipals’. Ignored otherwise.
pub fn tolerance(self, tolerance: F) -> Self
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Set the tolerance used as convergence criteria in the power method: the algorithm stops whenever the squared norm of u_i - u_{i-1} is less than tol, where u corresponds to the left singular vector.
pub fn scale(self, scale: bool) -> Self
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Set whether to scale the dataset
pub fn algorithm(self, algorithm: Algorithm) -> Self
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Set the algorithm used to estimate the first singular vectors of the cross-covariance matrix.
Nipals
uses the power method while Svd
will compute the whole SVD.
Trait Implementations
Auto Trait Implementations
impl<F> RefUnwindSafe for PlsRegressionParams<F> where
F: RefUnwindSafe,
impl<F> RefUnwindSafe for PlsRegressionParams<F> where
F: RefUnwindSafe,
impl<F> Send for PlsRegressionParams<F>
impl<F> Send for PlsRegressionParams<F>
impl<F> Sync for PlsRegressionParams<F>
impl<F> Sync for PlsRegressionParams<F>
impl<F> Unpin for PlsRegressionParams<F> where
F: Unpin,
impl<F> Unpin for PlsRegressionParams<F> where
F: Unpin,
impl<F> UnwindSafe for PlsRegressionParams<F> where
F: UnwindSafe,
impl<F> UnwindSafe for PlsRegressionParams<F> where
F: UnwindSafe,