advanced_algorithms::optimization::gradient_descent

Struct StochasticGD

pub struct StochasticGD { /* private fields */ }

Expand description

Stochastic Gradient Descent for batch optimization

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impl StochasticGD

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pub fn new(batch_size: usize) -> Self

Creates a new SGD optimizer

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pub fn minimize<G>( &self, grad_f: G, initial: &[f64], n_samples: usize, ) -> Result<OptimizationResult>
where G: Fn(&[f64], &[usize]) -> Vec<f64> + Sync,

Minimizes using mini-batch gradient descent

§Arguments

grad_f - Gradient function that takes parameters and data indices
initial - Initial parameter values
n_samples - Total number of training samples

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impl UnwindSafe for StochasticGD

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impl<T> Any for T
where T: 'static + ?Sized,

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impl<T> BorrowMut<T> for T
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fn borrow_mut(&mut self) -> &mut T

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impl<T> From<T> for T

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fn from(t: T) -> T

Returns the argument unchanged.

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impl<T, U> Into for T
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fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

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impl<T> IntoEither for T

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fn into_either(self, into_left: bool) -> Either<Self, Self>

Converts self into a Left variant of Either<Self, Self> if into_left is true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more

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fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
where F: FnOnce(&Self) -> bool,

Converts self into a Left variant of Either<Self, Self> if into_left(&self) returns true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more

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