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added early exit from main loop for semistable final states

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This commit is contained in:
Kevin D
2025-11-24 23:34:43 -08:00
parent 297ddbf160
commit 48763eb78c
4 changed files with 30 additions and 7 deletions
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4
@miSim/miSim.m
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@@ -14,6 +14,9 @@ classdef miSim
sensorPerformanceMinimum = 1e-6; % minimum sensor performance to allow assignment of a point in the domain to a partition
partitioning = NaN;
performance = NaN; % current cumulative sensor performance
oldMeanTotalPerf = 0;
fPerf; % performance plot figure
end
properties (Access = private)
@@ -29,7 +32,6 @@ classdef miSim
graphPlot; % objects for abstract network graph plot
partitionPlot; % objects for partition plot
fPerf; % performance plot figure
performancePlot; % objects for sensor performance plot
% Indicies for various plot types in the main tiled layout figure

14
@miSim/run.m
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@@ -10,6 +10,7 @@ function [obj] = run(obj)
v = obj.setupVideoWriter();
v.open();
steady = 0;
for ii = 1:size(obj.times, 1)
% Display current sim time
obj.t = obj.times(ii);
@@ -18,6 +19,19 @@ function [obj] = run(obj)
% Check if it's time for new partitions
updatePartitions = false;
if ismember(obj.t, obj.partitioningTimes)
% Check if it's time to end the sim (performance has settled)
if obj.t >= obj.partitioningTimes(5)
idx = find(obj.t == obj.partitioningTimes);
newMeanTotalPerf = mean(obj.perf(end, ((idx - 5 + 1):idx)));
if (obj.oldMeanTotalPerf * 0.95 <= newMeanTotalPerf) && (newMeanTotalPerf <= max(1e-6, obj.oldMeanTotalPerf * 1.05))
steady = steady + 1;
if steady >= 3
fprintf("Performance is stable, terminating early at %4.2f (%d/%d)\n", obj.t, ii, obj.maxIter + 1);
break; % performance is not improving further, exit main sim loop
end
end
obj.oldMeanTotalPerf = newMeanTotalPerf;
end
updatePartitions = true;
obj = obj.partition();
end

15
test/test_miSim.m
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@@ -104,10 +104,11 @@ classdef test_miSim < matlab.unittest.TestCase
if ii == 1
while agentsCrowdObjective(tc.domain.objective, candidatePos, mean(tc.domain.dimensions) / 2)
candidatePos = tc.domain.random();
candidatePos(3) = 2 + rand * 1.5; % place agents at decent altitudes for sensing
candidatePos(3) = 1 + rand * 3; % place agents at decent altitudes for sensing
end
else
candidatePos = tc.agents{randi(ii - 1)}.pos + sign(randn([1, 3])) .* (rand(1, 3) .* tc.comRange/sqrt(2));
candidatePos(3) = 1 + rand * 3; % place agents at decent altitudes for sensing
end
% Make sure that the candidate position is within the
@@ -239,6 +240,7 @@ classdef test_miSim < matlab.unittest.TestCase
end
else
candidatePos = tc.agents{randi(ii - 1)}.pos + sign(randn([1, 3])) .* (rand(1, 3) .* tc.comRange/sqrt(2));
candidatePos(3) = min([tc.domain.maxCorner(3) * 0.95, 0.5 + rand * (tc.alphaDistMax * (1.1) - 0.5)]); % place agents at decent altitudes for sensing
end
% Make sure that the candidate position is within the
@@ -359,10 +361,12 @@ classdef test_miSim < matlab.unittest.TestCase
sensor = sigmoidSensor;
% Homogeneous sensor model parameters
sensor = sensor.initialize(2.75, 9, NaN, NaN, 22.5, 9);
f = sensor.plotParameters();
% Heterogeneous sensor model parameters
% sensor = sensor.initialize(tc.alphaDistMin + rand * (tc.alphaDistMax - tc.alphaDistMin), tc.betaDistMin + rand * (tc.betaDistMax - tc.betaDistMin), NaN, NaN, tc.alphaTiltMin + rand * (tc.alphaTiltMax - tc.alphaTiltMin), tc.betaTiltMin + rand * (tc.betaTiltMax - tc.betaTiltMin));
% Plot sensor parameters (optional)
% f = sensor.plotParameters();
% Initialize agents
tc.agents = {agent; agent};
tc.agents{1} = tc.agents{1}.initialize(tc.domain.center + dh + [d, 0, 0], zeros(1,3), 0, 0, geometry1, sensor, @gradientAscent, 3*d, 1, sprintf("Agent %d", 1));
@@ -376,6 +380,7 @@ classdef test_miSim < matlab.unittest.TestCase
% Initialize the simulation
tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.timestep, tc.partitoningFreq, tc.maxIter);
close(tc.testClass.fPerf);
end
function test_single_partition(tc)
% make basic domain
@@ -383,7 +388,7 @@ classdef test_miSim < matlab.unittest.TestCase
tc.domain = tc.domain.initialize([zeros(1, 3); l * ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
% make basic sensing objective
tc.domain.objective = tc.domain.objective.initialize(@(x, y) mvnpdf([x(:), y(:)], tc.domain.center(1:2)), tc.domain, tc.discretizationStep, tc.protectedRange);
tc.domain.objective = tc.domain.objective.initialize(@(x, y) mvnpdf([x(:), y(:)], tc.domain.center(1:2) + rand(1, 2) * 6 - 3), tc.domain, tc.discretizationStep, tc.protectedRange);
% Initialize agent collision geometry
geometry1 = rectangularPrism;
@@ -395,6 +400,8 @@ classdef test_miSim < matlab.unittest.TestCase
% sensor = sensor.initialize(2.5666, 5.0807, NaN, NaN, 20.8614, 13); % 13
alphaDist = l/2; % half of domain length/width
sensor = sensor.initialize(alphaDist, 3, NaN, NaN, 20, 3);
% Plot sensor parameters (optional)
f = sensor.plotParameters();
% Initialize agents
@@ -403,7 +410,7 @@ classdef test_miSim < matlab.unittest.TestCase
% Initialize the simulation
tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.timestep, tc.partitoningFreq, tc.maxIter);
close(tc.testClass.fPerf);
end
end

4
test/test_sigmoidSensor.m
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@@ -36,8 +36,8 @@ classdef test_sigmoidSensor < matlab.unittest.TestCase
h = 1e-6;
tc.testClass = tc.testClass.initialize(alphaDist, betaDist, NaN, NaN, alphaTilt, betaTilt);
% Plot
tc.testClass.plotParameters();
% Plot (optional)
% tc.testClass.plotParameters();
% Anticipate perfect performance for a point directly below and
% extremely close