Enum abd_clam::Cakes

pub enum Cakes<I: Instance, U: Number, D: Dataset<I, U>> {
    SingleShard(SingleShard<I, U, D>),
    RandomlySharded(RandomlySharded<I, U, D>),
}

CAKES search.

Variants

SingleShard(SingleShard<I, U, D>)

Search with a single shard.

RandomlySharded(RandomlySharded<I, U, D>)

Search with multiple shards.

Implementations

impl<I: Instance, U: Number, D: Dataset<I, U>> Cakes<I, U, D>

pub fn new<P: PartitionCriterion<U>>( data: D, seed: Option<u64>, criteria: &P ) -> Self

Creates a new CAKES instance with a single shard dataset.

Arguments

• data - The dataset to search.
• seed - The seed to use for the random number generator.
• criteria - The criteria to use for partitioning the tree.

pub fn save(&self, path: &Path) -> Result<(), String>

Saves the Cakes structure to the given path.

Arguments

• path - The path to save the Cakes structure to.

Errors

• If the path does not exist.
• If the path is not a valid directory.

pub fn load( path: &Path, metric: fn(_: &I, _: &I) -> U, is_expensive: bool ) -> Result<Self, String>

Loads the Cakes structure from the given path.

Arguments

• path - The path to load the Cakes structure from.
• metric - The metric to use for the search.
• is_expensive - Whether the metric is expensive to compute.

Returns

The Cakes structure.

Errors

• If the path does not exist.
• If the path is not a valid directory.
• If the path does not contain a valid Cakes structure.

pub fn trees(&self) -> Vec<&Tree<I, U, D, UniBall<U>>>

Returns the references to the tree(s) of the dataset.

pub fn shards(&self) -> Vec<&D>

Returns the references to the shard(s) of the dataset.

pub fn new_randomly_sharded<P: PartitionCriterion<U>>( shards: Vec<D>, seed: Option<u64>, criteria: &P ) -> Self

Creates a new CAKES instance with a randomly sharded dataset.

Arguments

• shards - The shards of the dataset to search.
• seed - The seed to use for the random number generator.
• criteria - The criteria to use for partitioning the tree.

pub fn num_shards(&self) -> usize

Returns the number of shards in the dataset.

pub fn shard_cardinalities(&self) -> Vec<usize>

Returns the cardinalities of the shards in the dataset.

pub fn total_cardinality(&self) -> usize

Returns the total cardinality of the dataset.

pub fn tuned_rnn_algorithm(&self) -> Algorithm

Returns the tuned RNN algorithm.

pub fn batch_rnn_search( &self, queries: &[&I], radius: U, algo: Algorithm ) -> Vec<Vec<(usize, U)>>

Performs RNN search on a batch of queries with the given algorithm.

Arguments

• queries - The queries to search.
• radius - The search radius.
• algo - The algorithm to use.

Returns

A vector of vectors of tuples containing the index of the instance and the distance to the query.

pub fn rnn_search( &self, query: &I, radius: U, algo: Algorithm ) -> Vec<(usize, U)>

Performs an RNN search with the given algorithm.

Arguments

• query - The query instance.
• radius - The search radius.
• algo - The algorithm to use.

Returns

A vector of tuples containing the index of the instance and the distance to the query.

pub fn batch_linear_rnn_search( &self, queries: &[&I], radius: U ) -> Vec<Vec<(usize, U)>>

Performs Linear RNN search on a batch of queries.

Arguments

• queries - The queries to search.
• radius - The search radius.

Returns

A vector of vectors of tuples containing the index of the instance and the distance to the query.

pub fn linear_rnn_search(&self, query: &I, radius: U) -> Vec<(usize, U)>

Performs a linear RNN search.

Arguments

• query - The query instance.
• radius - The search radius.

Returns

A vector of tuples containing the index of the instance and the distance to the query.

pub fn tuned_knn_algorithm(&self) -> Algorithm

Returns the tuned KNN algorithm.

pub fn batch_knn_search( &self, queries: &[&I], k: usize, algo: Algorithm ) -> Vec<Vec<(usize, U)>>

Performs KNN search on a batch of queries with the given algorithm.

Arguments

• queries - The queries to search.
• k - The number of nearest neighbors to return.
• algo - The algorithm to use.

Returns

A vector of vectors of tuples containing the index of the instance and the distance to the query.

pub fn knn_search( &self, query: &I, k: usize, algo: Algorithm ) -> Vec<(usize, U)>

Performs a KNN search with the given algorithm.

Arguments

• query - The query instance.
• k - The number of nearest neighbors to return.
• algo - The algorithm to use.

Returns

A vector of tuples containing the index of the instance and the distance to the query.

pub fn auto_tune_rnn(&mut self, radius: U, tuning_depth: usize)

Automatically finds the best RNN algorithm to use.

Arguments

• radius - The search radius.
• tuning_depth - The number of instances to use for tuning.

pub fn auto_tune_knn(&mut self, k: usize, tuning_depth: usize)

Automatically finds the best KNN algorithm to use.

Arguments

• k - The number of nearest neighbors to return.
• tuning_depth - The number of instances to use for tuning.

pub fn batch_linear_knn_search( &self, queries: &[&I], k: usize ) -> Vec<Vec<(usize, U)>>

Performs Linear KNN search on a batch of queries.

Arguments

• queries - The queries to search.
• k - The number of nearest neighbors to return.

Returns

A vector of vectors of tuples containing the index of the instance and the distance to the query.

pub fn linear_knn_search(&self, query: &I, k: usize) -> Vec<(usize, U)>

Performs a linear KNN search.

Arguments

• query - The query instance.
• k - The number of nearest neighbors to return.

Returns

A vector of tuples containing the index of the instance and the distance to the query.

pub fn batch_tuned_rnn_search( &self, queries: &[&I], radius: U ) -> Vec<Vec<(usize, U)>>

Performs RNN search on a batch of queries with the tuned algorithm.

If the algorithm has not been tuned, this will use the default algorithm.

Arguments

• queries - The queries to search.
• radius - The search radius.

Returns

A vector of vectors of tuples containing the index of the instance and the distance to the query.

pub fn tuned_rnn_search(&self, query: &I, radius: U) -> Vec<(usize, U)>

Performs a RNN search with the tuned algorithm.

If the algorithm has not been tuned, this will use the default algorithm.

Arguments

• query - The query instance.
• radius - The search radius.

Returns

A vector of tuples containing the index of the instance and the distance to the query.

pub fn batch_tuned_knn_search( &self, queries: &[&I], k: usize ) -> Vec<Vec<(usize, U)>>

Performs KNN search on a batch of queries with the tuned algorithm.

If the algorithm has not been tuned, this will use the default algorithm.

Arguments

• queries - The queries to search.
• k - The number of nearest neighbors to return.

Returns

A vector of vectors of tuples containing the index of the instance and the distance to the query.

pub fn tuned_knn_search(&self, query: &I, k: usize) -> Vec<(usize, U)>

Performs a KNN search with the tuned algorithm.

If the algorithm has not been tuned, this will use the default algorithm.

Arguments

• query - The query instance.
• k - The number of nearest neighbors to return.

Returns

A vector of tuples containing the index of the instance and the distance to the query.

Trait Implementations

impl<I, U, D> Index<usize> for Cakes<I, U, D>

where I: Instance, U: Number, D: Dataset<I, U>,

type Output = I

The returned type after indexing.

fn index(&self, index: usize) -> &Self::Output

Performs the indexing (container[index]) operation.