Struct linfa_nn::LinearSearch [−][src]
pub struct LinearSearch;
Expand description
Implementation of linear search, which is the simplest nearest neighbour algorithm. All queries
are implemented by scanning through every point, so all of them are O(N)
. Calling
from_batch
returns a LinearSearchIndex
.
Implementations
Trait Implementations
Returns the “default value” for a type. Read more
fn from_batch_with_leaf_size<'a, F: Float, DT: Data<Elem = F>, D: 'a + Distance<F>>(
&self,
batch: &'a ArrayBase<DT, Ix2>,
leaf_size: usize,
dist_fn: D
) -> Result<Box<dyn NearestNeighbourIndex<F> + 'a>, BuildError>
fn from_batch_with_leaf_size<'a, F: Float, DT: Data<Elem = F>, D: 'a + Distance<F>>(
&self,
batch: &'a ArrayBase<DT, Ix2>,
leaf_size: usize,
dist_fn: D
) -> Result<Box<dyn NearestNeighbourIndex<F> + 'a>, BuildError>
Builds a spatial index using a MxN two-dimensional array representing M points with N
dimensions. Also takes leaf_size
, which specifies the number of elements in the leaf
nodes of tree-like index structures. Read more
fn from_batch<'a, F: Float, DT: Data<Elem = F>, D: 'a + Distance<F>>(
&self,
batch: &'a ArrayBase<DT, Ix2>,
dist_fn: D
) -> Result<Box<dyn NearestNeighbourIndex<F> + 'a>, BuildError>
fn from_batch<'a, F: Float, DT: Data<Elem = F>, D: 'a + Distance<F>>(
&self,
batch: &'a ArrayBase<DT, Ix2>,
dist_fn: D
) -> Result<Box<dyn NearestNeighbourIndex<F> + 'a>, BuildError>
Builds a spatial index using a default leaf size. See from_batch_with_leaf_size
for more
information. Read more
Auto Trait Implementations
impl RefUnwindSafe for LinearSearch
impl Send for LinearSearch
impl Sync for LinearSearch
impl Unpin for LinearSearch
impl UnwindSafe for LinearSearch
Blanket Implementations
Mutably borrows from an owned value. Read more