Struct FromLandscape 

pub struct FromLandscape<L> { /* private fields */ }

Adapter from Landscape to BatchFitnessFn<B, Tensor<B, 2>>.

Use this when the landscape carries its own evaluate(&[f64]) -> f64 (Sphere, Ackley, Rastrigin) so the example does not need a separate FitnessEvaluable shim. Each row is pulled to host as f32, widened to f64, evaluated, and re-uploaded as a Tensor<B, 1> — same precision caveats as FromFitnessEvaluable apply.

Panics

evaluate_batch panics if the supplied population tensor is not rank 2, or if its data cannot be read as f32 (e.g. an integer backend).

Implementations

impl<L: Landscape> FromLandscape<L>

pub fn new(landscape: L) -> Self

Builds the adapter from a self-evaluating landscape, taking the objective sense from the landscape’s Landscape::sense (which defaults to ObjectiveSense::Minimize).

pub fn with_sense(landscape: L, sense: ObjectiveSense) -> Self

Builds the adapter with an explicit ObjectiveSense, overriding the landscape’s declared sense. Examples and showcases spell out ObjectiveSense::Minimize here so intent is visible at the call site.

pub fn landscape(&self) -> &L

Returns a reference to the wrapped landscape.

Trait Implementations

impl<L, B> BatchFitnessFn<B, Tensor<B, 2>> for FromLandscape<L>

where B: Backend, L: Landscape,

fn evaluate_batch( &mut self, population: &Tensor<B, 2>, device: &<B as BackendTypes>::Device, ) -> Tensor<B, 1>

Evaluates every member of population and returns a fitness tensor in the objective’s natural value space (no hand-negation).

fn sense(&self) -> ObjectiveSense

The optimisation direction of this objective.

impl<L: Debug> Debug for FromLandscape<L>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.