36 lines
1.6 KiB
Matlab
36 lines
1.6 KiB
Matlab
function obj = partition(obj)
|
|
arguments (Input)
|
|
obj (1, 1) {mustBeA(obj, 'miSim')};
|
|
end
|
|
arguments (Output)
|
|
obj (1, 1) {mustBeA(obj, 'miSim')};
|
|
end
|
|
|
|
% Assess sensing performance of each agent at each sample point
|
|
% in the domain
|
|
agentPerformances = cellfun(@(x) reshape(x.sensorModel.sensorPerformance(x.pos, x.pan, x.tilt, [obj.objective.X(:), obj.objective.Y(:), zeros(size(obj.objective.X(:)))]), size(obj.objective.X)), obj.agents, 'UniformOutput', false);
|
|
agentPerformances{end + 1} = obj.sensorPerformanceMinimum * ones(size(agentPerformances{end})); % add additional layer to represent the threshold that has to be cleared for assignment to any partiton
|
|
agentPerformances = cat(3, agentPerformances{:});
|
|
|
|
% Get highest performance value at each point
|
|
[~, idx] = max(agentPerformances, [], 3);
|
|
|
|
% Collect agent indices in the same way as performance
|
|
agentInds = cellfun(@(x) x.index * ones(size(obj.objective.X)), obj.agents, 'UniformOutput', false);
|
|
agentInds{end + 1} = zeros(size(agentInds{end})); % index for no assignment
|
|
agentInds = cat(3, agentInds{:});
|
|
|
|
% Get highest performing agent's index
|
|
[m,n,~] = size(agentInds);
|
|
[jj,kk] = ndgrid(1:m, 1:n);
|
|
obj.partitioning = agentInds(sub2ind(size(agentInds), jj, kk, idx));
|
|
|
|
% Get individual agent sensor performance
|
|
nowIdx = [0; obj.partitioningTimes] == obj.t;
|
|
for ii = 1:size(obj.agents, 1)
|
|
obj.perf(ii, nowIdx) = sum(agentPerformances(sub2ind(size(agentInds), jj, kk, ii)), 'all');
|
|
end
|
|
|
|
% Current total performance
|
|
obj.perf(end, nowIdx) = sum(obj.perf(1:(end - 1), nowIdx));
|
|
end |