Hi Merlyn,
Here is an sample example for the same:
Generate Data and Hash Functions:
Create a sample dataset and define hash functions. These hash functions will map similar data points to the same bucket.
data = rand(100, 2); % 100 2D data points
numHashTables = 5;
numHashFunctions = 4;
hashFunctions = randn(numHashFunctions, 2, numHashTables);
Create Hash Tables:
Initialize hash tables to store indices of data points. Each table corresponds to a set of hash functions.
hashTables = cell(1, numHashTables);
for i = 1:numHashTables
hashTables{i} = containers.Map('KeyType', 'char', 'ValueType', 'any');
end
Populate Hash Tables:
For each data point, compute the hash keys using the hash functions and store the indices in the corresponding hash table.
for i = 1:size(data, 1)
for j = 1:numHashTables
hashKey = sign(hashFunctions(:, :, j) * data(i, :)');
keyStr = num2str(hashKey');
if isKey(hashTables{j}, keyStr)
hashTables{j}(keyStr) = [hashTables{j}(keyStr), i];
else
hashTables{j}(keyStr) = i;
end
end
end
Search for Approximate Nearest Neighbors:
To find ANNF, compute the hash keys for the query point and retrieve potential neighbors from the hash tables.
queryPoint = [0.5, 0.5];
candidateIndices = [];
for j = 1:numHashTables
hashKey = sign(hashFunctions(:, :, j) * queryPoint');
keyStr = num2str(hashKey');
if isKey(hashTables{j}, keyStr)
candidateIndices = [candidateIndices, hashTables{j}(keyStr)];
end
end
candidateIndices = unique(candidateIndices);
Evaluate Neighbors:
Compute distances from the query point to the candidates and select the nearest ones.
distances = vecnorm(data(candidateIndices, :) - queryPoint, 2, 2);
[~, sortedIndices] = sort(distances);
nearestNeighbors = candidateIndices(sortedIndices(1:5)); % Top 5 neighbors
Refer to the documentation of "containers.Map" function to know more about the properties supported: https://www.mathworks.com/help/matlab/ref/containers.map.html
Hope this helps!
