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reimplemented gradient ascent as central finite differences method

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Kevin Dee
2026-01-11 12:42:48 -08:00
parent c47b7229ba
commit ec202d7790
12 changed files with 101 additions and 176 deletions
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@miSim/partition.m
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@@ -9,7 +9,7 @@ function obj = partition(obj)
% 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{end + 1} = obj.domain.objective.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