'

@agent/partition.m

@@ -1,8 +1,8 @@
 function [partitioning] = partition(obj, agents, objective)
     arguments (Input)
-        obj (1, 1) {mustBeA(obj, 'agent')};
-        agents (:, 1) {mustBeA(agents, 'cell')};
-        objective (1, 1) {mustBeA(objective, 'sensingObjective')};
+        obj (1, 1) {mustBeA(obj, "agent")};
+        agents (:, 1) {mustBeA(agents, "cell")};
+        objective (1, 1) {mustBeA(objective, "sensingObjective")};
     end
     arguments (Output)
         partitioning (:, :) double;
@@ -10,7 +10,7 @@ function [partitioning] = partition(obj, agents, objective)
     
     % Assess sensing performance of each agent at each sample point
     % in the domain
-    agentPerformances = cellfun(@(x) reshape(x.sensorModel.sensorPerformance(x.pos, [objective.X(:), objective.Y(:), zeros(size(objective.X(:)))]), size(objective.X)), agents, 'UniformOutput', false);
+    agentPerformances = cellfun(@(x) reshape(x.sensorModel.sensorPerformance(x.pos, [objective.X(:), objective.Y(:), zeros(size(objective.X(:)))]), size(objective.X)), agents, "UniformOutput", false);
     agentPerformances{end + 1} = 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{:});
 
@@ -24,7 +24,7 @@ function [partitioning] = partition(obj, agents, objective)
         agentInds = reshape(agentInds, [size(agents, 1), size(agentInds)]); % needed for cases with 1 agent where prior squeeze is too agressive
     end
     agentInds = num2cell(agentInds, 2:3);
-    agentInds = cellfun(@(x) squeeze(x), agentInds, 'UniformOutput', false);
+    agentInds = cellfun(@(x) squeeze(x), agentInds, "UniformOutput", false);
     agentInds{end + 1} = zeros(size(agentInds{end})); % index for no assignment
     agentInds = cat(3, agentInds{:});