fn gradient<T, P, C>(cost: &C, prediction: &P, truth: T, derivative_of_model: &P) -> f64
where
    C: Cost<T, P>,
    P: Vector,
{
    cost.outer_derivative(&prediction, truth)
        .dot(derivative_of_model)
}
/// Value of the gradient of the cost function (i.e. the cost function derived by the n-th
/// coefficient at x expressed in Error(x) and dY(x)/dx
///
/// This method is called by stochastic gradient descent (SGD)-based training algorithm in order to
/// determine the delta of the coefficients
Value of the gradient of the cost function  and dY(x)/dx

This method is called by stochastic gradient descent (SGD)-based training algorithm in order to
determine the delta of the coefficients
Value of the gradient of the cost function and dY ( x ) / dx This method is called by stochastic gradient descent ( SGD ) - based training algorithm in order to determine the delta of the coefficients
fn gradient<T, P, C>(cost: &C, prediction: &P, truth: T, derivative_of_model: &P) -> f64
where
    C: Cost<T, P>,
    P: Vector,
{
    cost.outer_derivative(&prediction, truth)
        .dot(derivative_of_model)
}
fn gradient < T , P , C > ( cost : & C , prediction : & P , truth : T , derivative_of_model : & P ) -> f64 where C : Cost < T , P > , P : Vector , { cost . outer_derivative ( & prediction , truth ) . dot ( derivative_of_model ) }
Value of the gradient of the cost function (i.e.
Value of the gradient of the cost function ( i . e .
cost
&C
prediction
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truth
T
derivative_of_model
&P
cost
&C
prediction
&P
truth
T
derivative_of_model
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