Trait neuronika::VecVecMul

pub trait VecVecMul<Rhs> {
    type Output;
    fn vv(self, other: Rhs) -> Self::Output;
}

Vector-vector multiplication, a.k.a. dot product or inner product.

Associated Types

type Output

The type of the dot product’s result. See the differentiability arithmetic for more details.

Required methods

fn vv(self, other: Rhs) -> Self::Output

Computes the dot product between self and other.

Implementors

impl<F1, B1, F2> VecVecMul<Var<F2>> for VarDiff<F1, B1> where
    F1: Data<Dim = Ix1> + 'static,
    B1: Gradient<Dim = Ix1> + Overwrite + 'static,
    F2: Data<Dim = Ix1> + 'static, 

type Output = VarDiff<VectorVectorMul<F1, F2>, VectorVectorMulBackwardUnary<B1, F2>>

impl<F1, B1, F2, B2> VecVecMul<VarDiff<F2, B2>> for VarDiff<F1, B1> where
    F1: Data<Dim = Ix1> + 'static,
    B1: Gradient<Dim = Ix1> + Overwrite + 'static,
    F2: Data<Dim = Ix1> + 'static,
    B2: Gradient<Dim = Ix1> + Overwrite + 'static, 

type Output = VarDiff<VectorVectorMul<F1, F2>, VectorVectorMulBackward<F1, B1, F2, B2>>

impl<F1, F2> VecVecMul<Var<F2>> for Var<F1> where
    F1: Data<Dim = Ix1> + 'static,
    F2: Data<Dim = Ix1> + 'static, 

type Output = Var<VectorVectorMul<F1, F2>>

impl<F1, F2, B2> VecVecMul<VarDiff<F2, B2>> for Var<F1> where
    F1: Data<Dim = Ix1> + 'static,
    F2: Data<Dim = Ix1> + 'static,
    B2: Gradient<Dim = Ix1> + Overwrite + 'static, 

type Output = VarDiff<VectorVectorMul<F1, F2>, VectorVectorMulBackwardUnary<B2, F1>>