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Class Definition: hnswSearcher

Class: hnswSearcher

Hierarchical Navigable Small World (HNSW) nearest neighbor searcher class.

The hnswSearcher class implements the HNSW algorithm for efficient nearest neighbor queries. It stores training data and supports various distance metrics for performing searches. The HNSW algorithm builds a multilayer graph structure that enables fast approximate nearest neighbor searches by navigating through the graph. It facilitates a nearest neighbor search using knnsearch.

You can either use the hnswSearcher class constructor or the createns function to create an hnswSearcher object.

See also: createns, ExhaustiveSearcher, KDTreeSearcher, knnsearch

Source Code: hnswSearcher

Properties

  • For "minkowski", a positive scalar exponent (default 2).
  • For "seuclidean", a nonnegative vector of scaling factors matching the number of columns in X (default is standard deviation of X).
  • For "mahalanobis", a positive definite covariance matrix matching the dimensions of X (default is cov (X)).
  • Empty for other metrics.

Methods

hnswSearcher: obj = hnswSearcher (X)
hnswSearcher: obj = hnswSearcher (X, name, value)

obj = hnswSearcher (X) constructs an hnswSearcher object with training data X using the default "euclidean" distance metric. X must be an N×P numeric matrix, where rows represent observations and columns represent features.

obj = hnswSearcher (X, name, value) allows customization through name-value pairs:

NameValue
"Distance"Distance metric, specified as a character vector (e.g., "euclidean", "minkowski", "cityblock"). Default is "euclidean". See pdist2 for supported metrics.
"P"Minkowski distance exponent, a positive scalar. Valid only when "Distance" is "minkowski". Default is 2.
"Scale"Nonnegative vector of scaling factors matching the number of columns in X. Valid only when "Distance" is "seuclidean". Default is std (X).
"Cov"Positive definite covariance matrix matching the number of columns in X. Valid only when "Distance" is "mahalanobis". Default is cov (X).
"MaxNumLinksPerNode"Maximum number of neighbors per node in the HNSW graph, a positive integer. Default is 16.
"TrainSetSize"Size of the dynamic candidate list during graph construction, a positive integer. Default is 200.

See also: hnswSearcher, knnsearch, createns, pdist2

hnswSearcher: [idx, D] = knnsearch (obj, Y)
hnswSearcher: [idx, D] = knnsearch (obj, Y, name, value)

[idx, D] = knnsearch (obj, Y) returns the indices idx and distances D of the nearest neighbor in obj.X to each point in Y, using the distance metric specified in obj.Distance.

  • obj is an hnswSearcher object.
  • Y is an M×P numeric matrix of query points, where P must match the number of columns in obj.X.
  • idx contains the indices of the nearest neighbors in obj.X.
  • D contains the corresponding distances.

[idx, D] = knnsearch (obj, Y, name, value) allows additional options via name-value pairs:

NameValue
"K"A positive integer specifying the number of nearest neighbors to find. Default is 1.
"SearchSetSize"A positive integer specifying the size of the candidate list of nearest neighbors for a single query point during the search process. Default is max (10, C), where C is the number of columns in obj.X. "SearchSetSize" must be at least C and no more than the number of rows in training data obj.X.

See also: hnswSearcher, pdist2

Example: 1

  

 ## Create an hnswSearcher with Euclidean distance
 X = [1, 2; 3, 4; 5, 6];
 obj = hnswSearcher (X);
 ## Find the nearest neighbor to [2, 3]
 Y = [2, 3];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 disp ("Nearest neighbor index:");
 disp (idx);
 disp ("Distance:");
 disp (D);

Nearest neighbor index:
2
Distance:
1.4142

Example: 2

  

 ## Create an hnswSearcher with Minkowski distance (P=3)
 X = [0, 0; 1, 0; 2, 0];
 obj = hnswSearcher (X, "Distance", "minkowski", "P", 3);
 ## Find the nearest neighbor to [1, 0]
 Y = [1, 0];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 disp ("Nearest neighbor index:");
 disp (idx);
 disp ("Distance:");
 disp (D);

Nearest neighbor index:
2
Distance:
0