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lots of cleanup and simplification in test case construction

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This commit is contained in:
Kevin Dee
2026-01-13 21:17:35 -08:00
parent 08e396c155
commit bcb3bc3da3
65 changed files with 150 additions and 265 deletions
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8
@agent/run.m
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@@ -13,6 +13,10 @@ function obj = run(obj, domain, partitioning, timestepIndex, index, agents)
% Collect objective function values across partition
partitionMask = partitioning == index;
if ~unique(partitionMask)
% This agent has no partition, maintain current state
return;
end
objectiveValues = domain.objective.values(partitionMask); % f(omega) on W_n
% Compute sensor performance on partition
@@ -30,7 +34,7 @@ function obj = run(obj, domain, partitioning, timestepIndex, index, agents)
% Compute performance values on partition
if ii < 5
% Compute sensing performance
sensorValues = obj.sensorModel.sensorPerformance(pos, obj.pan, obj.tilt, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n) on W_n
sensorValues = obj.sensorModel.sensorPerformance(pos, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n) on W_n
% Objective performance does not change for 0, +/- X, Y steps.
% Those values are computed once before the loop and are only
% recomputed when +/- Z steps are applied
@@ -45,7 +49,7 @@ function obj = run(obj, domain, partitioning, timestepIndex, index, agents)
% Recompute partiton-derived performance values for sensing
maskedX = domain.objective.X(partitionMask);
maskedY = domain.objective.Y(partitionMask);
sensorValues = obj.sensorModel.sensorPerformance(pos, obj.pan, obj.tilt, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n) on W_n
sensorValues = obj.sensorModel.sensorPerformance(pos, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n) on W_n
end
% Rearrange data into image arrays