Struct ActiveLearner

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pub struct ActiveLearner<T>where
    T: Float + FromPrimitive + Debug + Display + AddAssign + SubAssign + MulAssign + DivAssign + RemAssign + Copy,
{ /* private fields */ }

Expand description

Active learning system for intelligent sampling in interpolation

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impl<T> ActiveLearner<T>
where T: Float + FromPrimitive + Debug + Display + AddAssign + SubAssign + MulAssign + DivAssign + RemAssign + Copy,

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pub fn new( interpolator: MultiscaleBSpline<T>, strategy: SamplingStrategy, ) -> Self

Create a new active learner with an initial interpolation model

§Arguments

interpolator - Initial interpolation model (typically trained on a small dataset)
strategy - Primary sampling strategy to use

§Returns

A new ActiveLearner instance ready for adaptive sampling

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pub fn with_budget(self, budget: usize) -> Self

Set the total sampling budget

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pub fn with_exploration_weight(self, weight: T) -> Self

Set the exploration weight (0.0 = pure exploitation, 1.0 = pure exploration)

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pub fn with_min_sample_distance(self, distance: T) -> Self

Set the minimum distance between new sampling points

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pub fn with_maxsamples_per_iteration(self, maxsamples: usize) -> Self

Set the maximum number of samples per iteration

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pub fn suggest_samples( &mut self, num_points: usize, ) -> InterpolateResult<Vec<SamplingCandidate<T>>>

Suggest new sampling points based on the current strategy

§Arguments

num_points - Number of new points to suggest (limited by maxsamples_per_iteration)

§Returns

A vector of candidate sampling points with their utility scores

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pub fn update_model( &mut self, new_x: &ArrayView1<'_, T>, new_y: &ArrayView1<'_, T>, ) -> InterpolateResult<bool>

Update the interpolation model with new data points

§Arguments

new_x - x-coordinates of new sample points
new_y - y-coordinates of new sample points

§Returns

true if the model was successfully updated

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pub fn get_stats(&self) -> &LearningStats

Get current learning statistics

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pub fn get_interpolator(&self) -> &MultiscaleBSpline<T>

Get the current interpolation model

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pub fn should_stop(&self) -> bool

Check if learning should stop based on convergence criteria

Trait Implementations§

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impl<T> Debug for ActiveLearner<T>
where T: Float + FromPrimitive + Debug + Display + AddAssign + SubAssign + MulAssign + DivAssign + RemAssign + Copy + Debug,

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

Formats the value using the given formatter. Read more

Auto Trait Implementations§

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impl<T> UnwindSafe for ActiveLearner<T>
where T: UnwindSafe + RefUnwindSafe,

Blanket Implementations§

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

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fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more

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impl<T> Borrow<T> for T
where T: ?Sized,

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fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more

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impl<T> BorrowMut<T> for T
where T: ?Sized,

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fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more

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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
where U: From<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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