added second plot - pairwise distances
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88
plot1.m
88
plot1.m
@@ -7,10 +7,14 @@ simHists = simHists(endsWith({simHists.name}, 'miSimHist.mat'));
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assert(length(simHists) == length(simInits), "input data availability mismatch");
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% Initialize plotting data
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Cfinal = NaN(12, 1);
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n = NaN(12, 1);
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doubleIntegrator = NaN(12, 1);
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numObjective = NaN(12, 1);
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nRuns = length(simHists);
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Cfinal = NaN(nRuns, 1);
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n = NaN(nRuns, 1);
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doubleIntegrator = NaN(nRuns, 1);
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numObjective = NaN(nRuns, 1);
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positions = cell(6, nRuns);
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commsRadius = NaN(nRuns, 1);
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collisionRadius = NaN(nRuns, 1);
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% Aggregate relevant data
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for ii = 1:length(simHists)
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@@ -26,11 +30,18 @@ for ii = 1:length(simHists)
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n(ii) = init.numAgents;
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doubleIntegrator(ii) = init.useDoubleIntegrator;
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numObjective(ii) = size(init.objectivePos, 1);
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commsRadius(ii) = unique(init.comRange);
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collisionRadius(ii) = unique(init.collisionRadius);
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for jj = 1:length(hist.out.agent)
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alphaDist(jj, ii) = hist.out.agent(jj).sensor.alphaDist;
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positions{jj, ii} = hist.out.agent(jj).pos;
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assert(hist.out.agent(jj).commsRadius == commsRadius(ii));
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assert(hist.out.agent(jj).collisionRadius == collisionRadius(ii));
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end
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end
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commsRadius = unique(commsRadius); assert(isscalar(commsRadius));
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collisionRadius = unique(collisionRadius); assert(isscalar(collisionRadius));
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sensors = unique(alphaDist(1, :));
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config = [];
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@@ -62,8 +73,8 @@ for ii = 1:length(simHists)
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end
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close all;
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f = figure;
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x = axes; grid(x, "on");
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f1 = figure;
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x1 = axes;
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n_unique = sort(unique(n));
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C = [];
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@@ -77,5 +88,68 @@ ylabel("Final coverage (fraction of maximum)");
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title("Final performance of parameterizations");
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legend(["$AI\alpha$"; "$AI\beta$"; "$AII\alpha$"; "$BI\beta$"], "Interpreter", "latex");
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grid("on");
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keyboard
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f2 = figure;
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x2 = axes;
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% Compute pairwise distances between agents in each column of positions
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% cell array
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% Compute pairwise distances between agents
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maxPairs = nchoosek(6, 2); % 15 pairs for max 6 agents
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pairDist = cell(maxPairs, nRuns);
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for ii = 1:nRuns
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pp = 0;
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for jj = 1:n(ii)-1
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for kk = jj+1:n(ii)
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pp = pp + 1;
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pairDist{pp, ii} = vecnorm(positions{jj, ii} - positions{kk, ii}, 2, 2);
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end
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end
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end
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% Compute mean, min, max pairwise distance across all pairs and timesteps per run
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meanPairDist = NaN(nRuns, 1);
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minPairDist = NaN(nRuns, 1);
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maxPairDist = NaN(nRuns, 1);
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for ii = 1:nRuns
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nPairs = nchoosek(n(ii), 2);
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D = vertcat(pairDist{1:nPairs, ii});
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meanPairDist(ii) = mean(D);
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minPairDist(ii) = min(D);
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maxPairDist(ii) = max(D);
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end
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% Group pairwise distance stats by n-value (same layout as bar plot)
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nConfigs = nRuns / length(n_unique);
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meanD = NaN(length(n_unique), nConfigs);
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minD = NaN(length(n_unique), nConfigs);
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maxD = NaN(length(n_unique), nConfigs);
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for ii = 1:length(n_unique)
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idx = find(n == n_unique(ii));
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meanD(ii, :) = meanPairDist(idx)';
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minD(ii, :) = minPairDist(idx)';
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maxD(ii, :) = maxPairDist(idx)';
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end
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% Plot whiskers (min to max) with mean markers
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nGroups = length(n_unique);
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barWidth = 0.8;
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groupWidth = barWidth / nConfigs;
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hold(x2, 'on');
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for jj = 1:nConfigs
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xPos = (1:nGroups) + (jj - (nConfigs + 1) / 2) * groupWidth;
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errorbar(x2, xPos, meanD(:, jj), meanD(:, jj) - minD(:, jj), maxD(:, jj) - meanD(:, jj), ...
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'o', 'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 10);
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end
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hold(x2, 'off');
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set(x2, 'XTick', 1:nGroups, 'XTickLabel', string(n_unique));
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xlabel(x2, "Number of agents");
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ylabel(x2, "Pairwise distance");
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title(x2, "Pairwise Agent Distances (min/mean/max)");
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legend(x2, ["$AI\alpha$"; "$AI\beta$"; "$AII\alpha$"; "$BI\beta$"], "Interpreter", "latex");
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grid(x2, "on");
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yline(collisionRadius, 'r--', "Label", "Collision Radius", "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
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yline(commsRadius, 'r--', "Label", "Communications Radius", "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
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ylim([0, commsRadius + 5]);
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@@ -10,7 +10,7 @@ classdef results < matlab.unittest.TestCase
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%% Diagnostic Parameters
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% No effect on simulation dynamics
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makeVideo = false; % disable video writing for big performance increase
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makePlots = false; % disable plotting for big performance increase (also disables video)
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makePlots = true; % disable plotting for big performance increase (also disables video)
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plotCommsGeometry = false; % disable plotting communications geometries
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%% Scenario Reinitialization
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@@ -54,17 +54,6 @@ classdef results < matlab.unittest.TestCase
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end
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end
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methods (TestMethodSetup)
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% % Generate a random domain for each test
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% function tc = setDomain(tc)
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% tc.testClass.domain = rectangularPrism;
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% % random integer-dimensioned cubic domain
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% tc.testClass.domain = tc.testClass.domain.initializeRandom(REGION_TYPE.DOMAIN, "Domain", tc.minDimension);
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% % Random bivariate normal PDF objective
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% tc.testClass.domain.objective = tc.testClass.domain.objective.initializeRandomMvnpdf(tc.testClass.domain, tc.discretizationStep, tc.protectedRange);
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% end
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end
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methods (Static, Access = private)
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function c = makeConfigs()
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rng(results.seed);
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