pub fn vector(basis: B, optimiser: O, n_outputs: usize) -> Self
    where
        B: spaces::Space,
    {
        let n: usize = basis.dim().into();
        let weights = ndarray::Array2::zeros((n, n_outputs));

        LFA {
            basis,
            weights,
            optimiser,
        }
    }
/// Construct an `VectorLFA` with a chosen number of outputs using a given
/// basis representation and optimisation routine. The weights are
/// initialised with a matrix of zeros.
Construct an `VectorLFA` with a chosen number of outputs using a given
basis representation and optimisation routine. The weights are
initialised with a matrix of zeros.
Construct an ` VectorLFA ` with a chosen number of outputs using a given basis representation and optimisation routine . The weights are initialised with a matrix of zeros .
pub fn vector(basis: B, optimiser: O, n_outputs: usize) -> Self
    where
        B: spaces::Space,
    {
        let n: usize = basis.dim().into();
        let weights = ndarray::Array2::zeros((n, n_outputs));
        LFA {
            basis,
            weights,
            optimiser,
        }
    }
pub fn vector ( basis : B , optimiser : O , n_outputs : usize ) -> Self where B : spaces :: Space , { let n : usize = basis . dim ( ) . into ( ) ; let weights = ndarray :: Array2 :: zeros ( ( n , n_outputs ) ) ; LFA { basis , weights , optimiser , } }
Construct an `VectorLFA` with a chosen number of outputs using a given
basis representation and optimisation routine.
Construct an ` VectorLFA ` with a chosen number of outputs using a given basis representation and optimisation routine .
basis
B
optimiser
O
n_outputs
usize
basis
B
optimiser
O
n_outputs
usize