Spell-checking comments

DRAsimulator.m

@@ -95,7 +95,7 @@ end
 toc;
 
 %% COMPUTE AND SHOW METRICS
-fResults = invertCellStruct(results);   % make a strcture with proper format for exploring the results
+fResults = invertCellStruct(results);   % make a structure with proper format for exploring the results
 fResults = applyMetric(fResults, @(s) 100 * double(s.numInfected) / graphPars.N, 'percentageInfected');
 fResults = applyMetric(fResults, @(s) 100 * double(s.numInfected(end)) / graphPars.N, 'finalPercentageInfected');
 fResults = applyMetric(fResults, @(s) sum(s.numInfected) * simulationPars.timeStep, 'AUC');

EvalMetrics/applyMetric.m

@@ -12,7 +12,7 @@
 %   - metricName: the name of computed evaluation metric.
 %
 % Output:
-%   - sResult: the udated structure with the simulation results.
+%   - sResult: the updated structure with the simulation results.
 %---
 % This is part of the DRA Simulator package for Dynamic Resource Allocation
 % strategies aiming to suppress SIS epidemics. See the comments header 

README.md

@@ -68,7 +68,7 @@ Project contents:
 
 `./EvalMetric/*`
 
-> functions that compute the evaluation metrics used to asses the quality of the compaired control strategies.
+> functions that compute the evaluation metrics used to asses the quality of the compared control strategies.
 
 `./GraphModels/*`
 

Strategies/createStrategies.m

 %createStrategies - This function creates one structure storing all the 
-%settings for the comtrol strategies that wil be simulated.
+%settings for the control strategies that will be simulated.
 %
 % function strategies = createStrategies (btot, varargin)
 %
@@ -16,7 +16,7 @@
 %        randomly chosen nodes. Note that this is a naive dynamic strategy
 %        which decides at random each time an event happens (infection/recovery). 
 %     --<Static strategies>--
-%     > 'RANDFIXED': it fixes the btot treatments to randomly selected 
+%     > 'RANDFIXED': it fixes the btot treatments to be randomly selected 
 %       nodes and keep it this way throughout all the simulation.
 %     > 'MN': favors the treatment of the nodes with most neighbors (high degree nodes).
 %     > 'LN': favors the treatment of the nodes with least neighbors (low degree nodes).
@@ -25,7 +25,7 @@
 %     > 'MSN' : the treatment resources are given to the btot nodes that
 %       have the most susceptible neighbors (i.e. to the most viral nodes)
 %     > 'LIN' : the treatment resources are given to the btot nodes that 
-%       have the least infected neighbors (i.e. to the most safe nodes)
+%       have the least infected neighbors (i.e. to the safest nodes)
 %     > 'LRIE': it roughly combines MSN and LIN. This is the optimal greedy
 %        control strategy.
 %

Strategies/dropInMaxEigenValueApprox.m

-%dropInMaxEigenValueApprox - An appriximation algorithm for the computation
+%dropInMaxEigenValueApprox - An approximation algorithm for the computation
 %of a score for each node that represents the drop of in the max eigenvalue 
 %observed after removing that node from the graph.
 %

Strategies/preferentialStrategy.m

 %preferentialStrategy - It creates a strategy object for those strategies
-%that apply a preferencial selection of nodes to distribute resources, 
+%that apply a preferential selection of nodes to distribute resources, 
 %according to a score function. Given an ordering of the nodes (specified 
 %by a score for each node), it selects the btot first out of them (that are 
 %also infected if amongInfected is set to true). 

Strategies/randomStrategy.m

@@ -10,7 +10,7 @@
 %   - strategyInternalState: a structure with all the internal information
 %     of the strategy state.
 %   - btot: the number of available treatment resources to distribute.
-%   - amongInfected: if true, it restict the distribution to resources to
+%   - amongInfected: if true, it restrict the distribution to resources to
 %     the infected nodes only.
 %
 % Output:
@@ -72,4 +72,4 @@ end
 
 treatments(idxToTreat(1:btot)) = true;
 
-end
\ No newline at end of file
+end

Var/invertCellStruct.m

 %invertCellStruct - A utility function that manipulates the original cell 
 %structure with the simulation results and makes a new structure with one
-%field per compared cotrol strategy.
+%field per compared control strategy.
 %
 % function s = invertCellStruct (c)
 %
 % Input:
-%   - c: the original cell marix storing simulation resuls.
+%   - c: the original cell matrix storing simulation results.
 %
 % Output:
 %   - s: a new structure with one filed per compared control strategy.

Var/parsepar.m

 %parsepar - Processes the list if variable arguments (varargin) of a function.
-%It seeks for a specific variable name in a par list (case-insesnsitive) 
+%It seeks for a specific variable name in a par list (case-insensitive) 
 %and returns the respective input value. It also returns an updated varlist
 %without the matched field name and val for quicker further processing.
 %

Var/randv_dpdf.cpp

 /* This mex file implements a random integer generator between elements 1...N
  * according to an input weight vector that is treated as a probability vector
  * (after considered the elements to be normalized wrt norm-1)
- * Copyrigth (C) Argyris Kalogeratos, 2014.
+ * Copyright (c) Argyris Kalogeratos, 2014.
  * ---
  * This is part of the DRA Simulator package for Dynamic Resource Allocation
  * strategies aiming to suppress SIS epidemics. See the comments header 
@@ -41,4 +41,4 @@ void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
     for (i=0; i<dimx && r>pdf[i]/sum; i++)
         ;
     *mxGetPr(plhs[0]) = i+1;
-}
\ No newline at end of file
+}

Var/varIntSize.m

-%varIntSize - Determines the type of integer variable needed in irder to 
+%varIntSize - Determines the type of integer variable needed in order to 
 %store values in [minv, maxv], signed or unsigned. This is used to make
 % proper preallocation of memory space.
 %

Visualization/plotMeanAndStd.m

@@ -45,7 +45,7 @@ for iCurves = 1:numCurves
 end
 
 % confidence intervals
-if (showStd) % avoid shoing std around lines if only one experiment was done
+if (showStd) % avoid showing std around lines if only one experiment was done
     for iCurves = 1:numCurves
         lowerBound = max(0, yMean(:, iCurves) - yStd(:, iCurves));
         upperBound = min(100, yMean(:, iCurves) + yStd(:, iCurves));

epidemicSimulation.m

@@ -16,7 +16,7 @@
 %   - simulationPars: a structure with simulation settings. 
 %     See simulateEpidemic() for more details.
 %   - varargin: a list of other optional arguments, here 'max_infected' is 
-%     the infection level over which we may decide for the absorbant state 
+%     the infection level over which we may decide for the absorbent state 
 %     (this is for the stationarity test used as stopping criterion).
 % Output:
 %   - epidemicEvolution: a structure with the recorded information during
@@ -56,7 +56,7 @@ else updateInterval = @() -1;             % will always update the resource allo
 end
 if (isfield(simulationPars, 'noSlopeT'))
      noSlopeT = simulationPars.noSlopeT;
-else noSlopeT = inf;                      % the stationarity test will be disactivated
+else noSlopeT = inf;                      % the stationarity test will be deactivated
 end
 if (isfield(simulationPars, 'stateSnapshots'))
      stateSnapshots = simulationPars.stateSnapshots;
@@ -110,7 +110,7 @@ while (currentTime <= T && ~stopSimulation && iter <= msz)
     
     transitionRate = sum(probTransition);  % overall rate of events
     
-    % Absorbant state: the system is now static and we can stop
+    % Absorbent state: the system is now static and we can stop
     if (transitionRate <= 0 || curNumInfected >= max_infected)
         numInfected(iter) = curNumInfected;
         if (stateSnapshots)
@@ -170,7 +170,7 @@ end
 %% SAVE RESULTS
 iter          = iter - 1;              % the last iteration
 range         = 1:iter;
-numInfected   = numInfected(range);    % keep the usefull part of the matrices
+numInfected   = numInfected(range);    % keep the useful part of the matrices
 timeSteps     = timeSteps(range);
 if (stateSnapshots)
     stateSnapshot = stateSnapshot(:, range);

simulateEpidemic.m

@@ -27,7 +27,7 @@
 %     > .T       : total simulation time (stopping criterion)
 %     > .noSlopeT: characteristic time for the non stationarity test used
 %       to determine when an experiment reached a saturation state. Set an 
-%       Inf value to ignore this feature and simulatie up to total time T.
+%       Inf value to ignore this feature and simulate up to total time T.
 %     > .timeStep: the time interval for the recording the infection network 
 %       state and other results (low values wrt T will reduce simulation speed).
 %     > .stateSnapshots: if true, then the treatment distribution and network 
@@ -37,7 +37,7 @@
 %   - strategies: a structure with the strategies to use in the simulations.
 %     See more details in the file createStrategies.m.
 %   - varargin: a list of other optional arguments, here 'verbosityLevel' 
-%     indicates the the detail of reported information to the terminal.
+%     indicates the detail of reported information to the terminal.
 %
 % Output:
 %   - results: it is a structure with all the recorded information during