minor code improvements

DRAsimulator.m

@@ -39,18 +39,24 @@ rngInfo = rng;
     graphPars = struct;
     
     % a. Generate graph
-        graphPars.N           = 100;           % the size of graph to generate
-        graphPars.graphType = 'erdosRenyi';    % available: 'erdosRenyi', 'preferentialAttachment', 'smallWorld', 'adjacencyMatrix'
+        graphPars.N         = 100;           % the size of graph to generate
+        graphPars.graphType = 'preferentialAttachment';  % available: 'erdosRenyi', 'preferentialAttachment', 'smallWorld', 'adjacencyMatrix'
+        
+        switch graphPars.graphType,  % specific parameters needed for some random graph models
+        case 'erdosRenyi'
+            graphPars.edge_prob = 0.1;
+        case 'preferentialAttachment'
+            graphPars.edge_num = 5;
+            graphPars.delta = 0.01;
+        case 'smallWorld'
+            graphPars.edge_num  = 5;  
+            graphPars.edge_prob = 0.05;                
+        end 
         
-        if (strcmp(graphPars.graphType, 'erdosRenyi') == 1)
-            graphPars.prob_arete_graph = 0.1;  % only for erdosRenyiGraph() generation
-        end
-            
     % b. Use an input graph
     %  - enter 'adjacencyMatrix' in graphPars.graphType
-    %  - include the following:
-    %  - load or create your graph G 
-        %    G = erdosRenyiGraph(graphPars.N, graphPars.prob_arete_graph);   % this is just an example
+    %  - load your graph G or create it include the following:
+        %    G = erdosRenyiGraph(graphPars.N, graphPars.edge_prob);   % this is just an example
         %    graphPars.adjacencyMatrix = G;
         %    graphPars.N = size(G,1);
 

EvalMetrics/showMeasures.m

@@ -18,8 +18,8 @@
 function showMeasures (fRresults, varargin)
 
 methodNames = fieldnames(fRresults);
-numMeasures = length(varargin);
-numMethods  = length(methodNames);
+numMeasures = numel(varargin);
+numMethods  = numel(methodNames);
 
 measures = cell(numMeasures, 1);
 for j=1:numMeasures

GraphModels/erdosRenyiGraph.m

 %erdosRenyiGraph - A generator for Erdos Renyi graphs.
 %
-% function G = erdosRenyiGraph (N, p)
+% function G = erdosRenyiGraph (N, edge_num)
 %
 % Input:
 %   - N: the size of the graph to generate.
-%   - p: the probability of creating each edge (i,j).
+%   - edge_prob: the probability of creating each edge (i,j).
 %
 % Output:
 %   - G: the generated graph.
@@ -17,14 +17,14 @@
 % Copyright (c) by Argyris Kalogeratos and Kevin Scaman, 2015.
 %---
 
-function G = erdosRenyiGraph (N, p)
+function G = erdosRenyiGraph (N, edge_num)
 
 G = rand(N);
 G = G + G';
-if (p <= 0.5)
-    G = G < sqrt(2 * p);
+if (edge_num <= 0.5)
+    G = G < sqrt(2 * edge_num);
 else
-    G = G >= sqrt(2 * (1 - p));
+    G = G >= sqrt(2 * (1 - edge_num));
 end
 G = G - diag(diag(G));
 G = sparse(G);

GraphModels/preferentialAttachmentGraph.m

 %preferentialAttachmentGraph - A generator for Preferential Attachment graphs.
 %
-% function G = preferentialAttachmentGraph (N, M, delta)
+% function G = preferentialAttachmentGraph (N, num_edges, delta)
 %
 % Input:
 %   - N: the size of the graph to generate.
-%   - M: is the number of esges to generate.
+%   - num_edges: is the number of edges to generate.
 %   - delta: is an extra additive weight that increases the probability of 
 %     creating an edge, for each node.
 %
@@ -19,19 +19,19 @@
 % Copyright (c) by Argyris Kalogeratos and Kevin Scaman, 2015.
 %---
 
-function G = preferentialAttachmentGraph (N, M, delta)
+function G = preferentialAttachmentGraph (N, num_edges, delta)
 
 if (nargin < 3)
     delta = 0;
 end
 
-G = spalloc(N, N, N*M);
+G = spalloc(N, N, N*num_edges);
 G(1, 2) = 1;
 G(2, 1) = 1;
 
 for i = 3:N
     numNeighbors = full(sum(G,1));
-    newNeighbors = randp(max(0, numNeighbors + delta), [M 1]);
+    newNeighbors = randp(max(0, numNeighbors + delta), [num_edges 1]);
     G(i, newNeighbors) = 1;
     G(newNeighbors, i) = 1;
 end

GraphModels/smallWorldGraph.m

 %smallWorldGraph - A generator for Small World graphs.
 %
-% function G = smallWorldGraph (N, k, p)
+% function G = smallWorldGraph (N, edge_num, edge_prob)
 %
 % Input:
 %   - N: the size of the graph to generate.
-%   - k: the size of the neighbors to connect each node (it assumes that 
+%   - edge_num: the size of the neighbors to connect each node (it assumes that 
 %     each node is located on the periphery of a ring, then k is the number 
 %     of nearest neighbors to connect with each node)
-%   - p: the probability of creating each edge (i,j) of the Erdos Renyi model.
+%   - edge_prob: the probability of creating each edge (i,j) of the Erdos Renyi model.
 %     the generator makes an OR between the graph generated with the ring
 %     layout and an Erdos Renyi graph.
 %
@@ -22,18 +22,18 @@
 % Copyright (c) by Argyris Kalogeratos and Kevin Scaman, 2015.
 %---
 
-function G = smallWorldGraph (N, k, p)
+function G = smallWorldGraph (N, edge_num, edge_prob)
 
 G = zeros(N,N);
-for i = 1:k
+for i = 1:edge_num
     G = G + diag(ones(N - i, 1), i);
 end
 
 G = G + G';
-G = max(G, erdosRenyiGraph(N, p));
-p = randperm(N);
-G = G(p,:);
-G = G(:,p);
+G = max(G, erdosRenyiGraph(N, edge_prob));
+edge_prob = randperm(N);
+G = G(edge_prob,:);
+G = G(:,edge_prob);
 G = sparse(G);
 
 end

Strategies/allStrategy.m

-%allStrategy - It creates a strategy object for the ALL strategy.
+%allStrategy - Creates a strategy object for the ALL strategy.
 %
 % function  [treatments, strategyInternalState] = ...
 %                                   allStrategy (~, strategyInternalState)
 %
 % Input:
-%   - <void par>: this is a dummy parameter for reasons of being compatible
-%     with other functions that create a strategy object.
+%   - <void par>: this is a dummy parameter for the function to be compatible
+%     with other functions that create strategy objects.
 %   - strategyInternalState: a structure with all the internal information
 %     of the strategy state.
 %

Strategies/preferentialStrategy.m

@@ -77,7 +77,7 @@ numNodesToUpdate = nnz(idxUpdatedNodeScores); % old: full(sum(idxUpdatedNodeScor
 if (numNodesToUpdate)
     % Update sorting:
     % idxSorted is a vector (e.g. [5; 10; 2]) with the ids of infected nodes ranked 
-    % acording to the nodeScore value. The ids correspond to the internal numbering of the subset
+    % according to the nodeScore value. The ids correspond to the internal numbering of the subset
     % of infected nodes. So the 3rd id might be the 15th node in the original 1:N numbering. 
     if (idxVariation ~= -1 && infections(idxVariation))
         idxRanked = [idxRanked; idxVariation];

Strategies/randomStrategy.m

@@ -29,18 +29,18 @@
 
 function [treatments, strategyInternalState] = randomStrategy (idxVariation, strategyInternalState, btot, amongInfected)
 
-% Initialization: computing scores.
+% Initialization: computing scores
 if(isfield(strategyInternalState, 'infectionSeed'))
     strategyInternalState.N = size(strategyInternalState.graph, 1);
     strategyInternalState.infections = strategyInternalState.infectionSeed;
     strategyInternalState.numInfected = nnz(strategyInternalState.infections);
         
-    % Cleaning the internal state.
+    % Cleaning the internal state
     strategyInternalState = rmfield(strategyInternalState, 'graph');
     strategyInternalState = rmfield(strategyInternalState, 'infectionSeed');
 end
 
-% Update infections if this is not the initialization step.
+% Update infections, if this is not the initialization step
 if(idxVariation ~= -1)
     nextState = ~strategyInternalState.infections(idxVariation);
     if (nextState)

Strategies/staticStrategy.m

@@ -32,7 +32,7 @@
 
 function [treatments, strategyInternalState] = staticStrategy(idxVariation, strategyInternalState, scoringFunction, btot, strategyName, amongInfected, isOrder)
 
-% Initialization: computing scores.
+% Initialization: computing scores
 if(isfield(strategyInternalState, 'infectionSeed'))
     strategyInternalState.N = size(strategyInternalState.graph, 1);
     strategyInternalState.infections = strategyInternalState.infectionSeed;
@@ -45,12 +45,12 @@ if(isfield(strategyInternalState, 'infectionSeed'))
         strategyInternalState.idxRanked = scores;
     end
     
-    % Cleaning the internal state.
+    % Cleaning the internal state
     strategyInternalState = rmfield(strategyInternalState, 'graph');
     strategyInternalState = rmfield(strategyInternalState, 'infectionSeed');
 end
 
-% Update infections if this is not the initialization step.
+% Update infections if this is not the initialization step
 if(idxVariation ~= -1)
     nextState = ~strategyInternalState.infections(idxVariation);
     if (nextState)

Var/density.m

-%hasselfloops - Calculates the density of the non-zero entries of a matrix.
+%density - Calculates the density of the non-zero entries of a matrix.
 %
 % function val = density (M)
 %

Var/graphProperties.m

@@ -17,7 +17,7 @@ function graphProperties (G)
 
 N = size(G,1);
 [E, sym] = numGraphEdges(G);
-k = 1;
+
 
 fprintf('-----------------------------------------------------------------\n');    
 fprintf('Properties | N: %g, E: %g\n', N, E);

Visualization/plotCurves.m

@@ -44,6 +44,7 @@ if (size(graphCurves, 3) > 1)
      curveStds  = 2 * std(graphCurves, 0, 3) / sqrt(numTests);
      showStd = 1;
 else showStd = 0;
+     curveStds = [];
 end
 plotMeanAndStd(time, curveMeans, curveStds, curveNames, varargin{:}, 'showStd', showStd);
 

Visualization/plotMeanAndStd.m

@@ -36,7 +36,7 @@ figure; hold on;
 if (foundTitle),  title(mytitle);   end
 if (foundxlabel), xlabel(myxlabel); end
 if (foundylabel), ylabel(myylabel); end
-if (foundshowStd), showStd = 1; end
+if (~foundshowStd && ~isempty(yStd)), showStd = 1; end
 
 color = distinguishable_colors(numCurves);
 

epidemicSimulation.m

@@ -74,7 +74,7 @@ if (~found || max_infected > N), max_infected = N + 1; end
 % Initializes the strategy's internal state by providing the graph and infection seed
 strategyInternalState = struct('graph', G, 'infectionSeed', infected, 'issymmetricG', issymmetric(G));
 
-if (strategyInternalState.issymmetricG)   % the function to find the nodes of G that are connected to node i (with in- or out-edges)
+if (strategyInternalState.issymmetricG)   % function which finds the nodes of G that are connected to node i (with in- or out-edges)
      strategyInternalState.nghbs = @(G,i)  G(:,i);
 else strategyInternalState.nghbs = @(G,i)  (G(:,i)~=0 | G(i, :)'~=0);
 end
@@ -127,10 +127,9 @@ while (currentTime <= T && ~stopSimulation && iter <= msz)
     % then write results for all the time steps which are smaller than currentTime.
     diffInTimes = currentTime - timeSteps(iter);
     if (diffInTimes >= 0)
-        
         numStepsWithNoChange = ceil(diffInTimes / timeStep);
         for i=1:numStepsWithNoChange,
-            % Good for frequent node state changes (usually numStepsWithNoChange == 1) and, actually, 
+            % Proper for frequent node state changes (usually numStepsWithNoChange == 1) and, actually, 
             % this is the case: more than one node changes to happen among two recording steps.
             numInfected(iter)   = curNumInfected;
             timeSteps(iter + 1) = timeSteps(iter) + timeStep;
@@ -147,13 +146,13 @@ while (currentTime <= T && ~stopSimulation && iter <= msz)
             if (numSteps >= k_steps && curNumInfected > 20)  % k_steps begins from a large value, e.g. 100
                 steps = (floor(1/5 * iter):iter)';
                 stopSimulation = ~isNonStationaryTest(steps, double(numInfected(steps)'), double(curNumInfected)/10 * timeStep/noSlopeT, 0.01);
-                k_steps = k_steps * 1.5;   % recheck for stationarity later, after so many steps
+                k_steps = k_steps * 1.5;   % recheck for stationarity later, after that many steps
             end
             iter = iter + 1;  % increase the iteration counter
         end
     end
     
-    % Create a transition event, given a vector with transition rates (by normalizing this vector we get a probabilit vector that sums to 1)
+    % Create a transition event, given a vector with transition rates (although they don't need to be normalized, if so, this vector becomes a probability vector)
     idxVariation  = rand_probv(probTransition);
     idxWasHealthy = noninfected(idxVariation);
     

simulateEpidemic.m

@@ -58,15 +58,15 @@ if (~found), verbosityLevel = 1; end
 
 %% 1. NETWORK SPECIFICATION
     switch graphPars.graphType
-      case 'erdosRenyi'
-        G = erdosRenyiGraph(graphPars.N, graphPars.prob_arete_graph);
-      case 'preferentialAttachment'
-        G = preferentialAttachmentGraph(graphPars.N, graphPars.m_arete_graph, graphPars.delta);
-      case 'smallWorld'
-        G = smallWorldGraph(graphPars.N, graphPars.m_arete_graph, graphPars.prob_arete_graph);
-      case 'adjacencyMatrix'
+    case 'erdosRenyi'
+        G = erdosRenyiGraph(graphPars.N, graphPars.edge_prob);
+    case 'preferentialAttachment'
+        G = preferentialAttachmentGraph(graphPars.N, graphPars.edge_num, graphPars.delta);
+    case 'smallWorld'
+        G = smallWorldGraph(graphPars.N, graphPars.edge_num, graphPars.edge_prob);
+    case 'adjacencyMatrix'
         G = graphPars.adjacencyMatrix; 
-      otherwise
+        otherwise
         error('The graphType field of graphPars should be either ''erdosRenyi'', ''preferentialAttachment'', ''smallWorld'', ''adjacencyMatrix''');
     end