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added z component to GA with constant partition

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Kevin Dee
2026-01-07 11:43:14 -08:00
parent 62e015da42
commit af6a0447a8
1 changed files with 17 additions and 7 deletions
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24
@agent/run.m
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@@ -17,22 +17,30 @@ function obj = run(obj, domain, partitioning, t, index)
% Compute sensor performance across partition % Compute sensor performance across partition
maskedX = domain.objective.X(partitionMask); maskedX = domain.objective.X(partitionMask);
maskedY = domain.objective.Y(partitionMask); maskedY = domain.objective.Y(partitionMask);
zFactor = 10;
sensorValues = obj.sensorModel.sensorPerformance(obj.pos, obj.pan, obj.tilt, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n) on W_n sensorValues = obj.sensorModel.sensorPerformance(obj.pos, obj.pan, obj.tilt, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n) on W_n
sensorValuesLower = obj.sensorModel.sensorPerformance(obj.pos - [0, 0, zFactor * domain.objective.discretizationStep], obj.pan, obj.tilt, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n - [0, 0, z]) on W_n
sensorValuesHigher = obj.sensorModel.sensorPerformance(obj.pos + [0, 0, zFactor * domain.objective.discretizationStep], obj.pan, obj.tilt, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n - [0, 0, z]) on W_n
% Put the values back into the form of the partition to enable basic operations on this data % Put the values back into the form of the partition to enable basic operations on this data
F = NaN(size(partitionMask)); F = NaN(size(partitionMask));
F(partitionMask) = objectiveValues; F(partitionMask) = objectiveValues;
S = NaN(size(partitionMask)); S = NaN(size(partitionMask));
Slower = S;
Shigher = S;
S(partitionMask) = sensorValues; S(partitionMask) = sensorValues;
Slower(partitionMask) = sensorValuesLower;
Shigher(partitionMask) = sensorValuesHigher;
% Find agent's performance % Find agent's performance
C = S.* F; C = S .* F;
obj.performance = [obj.performance, sum(C(~isnan(C)))]; obj.performance = [obj.performance, sum(C(~isnan(C)))]; % at current Z only
C = cat(3, Shigher, S, Slower) .* F;
% Compute gradient on agent's performance % Compute gradient on agent's performance
[gradCX, gradCY] = gradient(C, domain.objective.discretizationStep); % grad C [gradCX, gradCY, gradCZ] = gradient(C, domain.objective.discretizationStep); % grad C
gradC = cat(3, gradCX, gradCY, zeros(size(gradCX))); % temp zeros for gradCZ gradC = cat(4, gradCX, gradCY, gradCZ);
nGradC = vecnorm(gradC, 2, 3); nGradC = vecnorm(gradC, 2, 4);
if obj.debug if obj.debug
% Compute additional component-level values for diagnosing issues % Compute additional component-level values for diagnosing issues
@@ -111,14 +119,16 @@ function obj = run(obj, domain, partitioning, t, index)
end end
% Use largest grad(C) value to find the direction of the next position % Use largest grad(C) value to find the direction of the next position
[xNextIdx, yNextIdx] = find(nGradC == max(nGradC, [], 'all')); [xNextIdx, yNextIdx, zNextIdx] = ind2sub(size(nGradC), find(nGradC == max(nGradC, [], 'all')));
disp(zNextIdx)
% switch them % switch them
temp = xNextIdx; temp = xNextIdx;
xNextIdx = yNextIdx; xNextIdx = yNextIdx;
yNextIdx = temp; yNextIdx = temp;
roundingScale = 10^-log10(domain.objective.discretizationStep); roundingScale = 10^-log10(domain.objective.discretizationStep);
pNext = [floor(roundingScale .* mean(unique(domain.objective.X(:, xNextIdx))))./roundingScale, floor(roundingScale .* mean(unique(domain.objective.Y(yNextIdx, :))))./roundingScale, obj.pos(3)]; % have to do some unfortunate rounding here soemtimes zKey = zFactor * [1; 0; -1];
pNext = [floor(roundingScale .* mean(unique(domain.objective.X(:, xNextIdx))))./roundingScale, floor(roundingScale .* mean(unique(domain.objective.Y(yNextIdx, :))))./roundingScale, obj.pos(3) + zKey(zNextIdx)]; % have to do some unfortunate rounding here sometimes
% Determine next position % Determine next position
vDir = (pNext - obj.pos)./norm(pNext - obj.pos, 2); vDir = (pNext - obj.pos)./norm(pNext - obj.pos, 2);