[−][src]Struct arrsac::Config
Configuration for an ARRSAC instance.
Methods
impl Config
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pub fn max_candidate_hypotheses(self, value: usize) -> Self
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Number of hypotheses that will be generated for each block of data evaluated
Default: 50
pub fn block_size(self, value: usize) -> Self
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Number of data points evaluated before more hypotheses are generated
Default: 100
pub fn max_delta_estimations(self, value: usize) -> Self
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Number of times that the entire dataset is compared against a bad model to see the probability of inliers in a bad model
Default: 4
pub fn likelyhood_ratio_threshold(self, value: f32) -> Self
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Once a model reaches this level of unlikelyhood, it is rejected. Set this higher to make it less restrictive, usually at the cost of more execution time.
Increasing this will make it more likely to find a good result (unless it is set very high).
Decreasing this will speed up execution.
This ratio is not exposed as a parameter in the original paper, but is instead computed recursively for a few iterations. It is roughly equivalent to the reciprocal of the probability of rejecting a good model. You can use that to control the probability that a good model is rejected.
Default: 1e6
pub fn initial_epsilon(self, value: f32) -> Self
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Initial anticipated probability of an inlier being part of a good model
This is an estimation that will be updated as ARRSAC executes. The initial estimate is purposefully low, which will accept more models. As models are accepted, it will gradually increase it to match the best model found so far, which makes it more restrictive.
Default: 0.1
pub fn initial_delta(self, value: f32) -> Self
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Initial anticipated probability of an inlier being part of a bad model
This is an estimation that will be updated as ARRSAC executes. The initial
estimate is almost certainly incorrect. This can be modified for different data
to get better/faster results. As models are rejected, it will update this value
until it has evaluated it max_delta_estimations
times.
Default: 0.05
pub fn inlier_threshold(self, value: f32) -> Self
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Residual threshold for determining if a data point is an inlier or an outlier of a model
impl Config
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pub fn new(inlier_threshold: f32) -> Self
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The inlier_threshold
is the one parameter that is always specific to your dataset.
This must be set to the threshold in which a data point's residual is considered an inlier.
Some of the other parameters may need to be configured based on the amount of data,
such as block_size
, likelyhood_ratio_threshold
, and block_size
. However,
inlier_threshold
has to be set based on the residual function used with the model.
Auto Trait Implementations
impl Send for Config
impl Sync for Config
impl Unpin for Config
impl UnwindSafe for Config
impl RefUnwindSafe for Config
Blanket Implementations
impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T> From<T> for T
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impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,
type Error = <U as TryFrom<T>>::Error
The type returned in the event of a conversion error.
fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>
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impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
fn borrow_mut(&mut self) -> &mut T
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impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<V, T> VZip<V> for T where
V: MultiLane<T>,
V: MultiLane<T>,