matlab_ICA与K-mean的混合改进K-mean算法

function [BestSolution BestCost] = TrainUsingICA_Fcn(data,k,d,n) %% Imperialist Competitive Algorithm (CCA); % A Socio Politically Inspired Optimization Strategy. % 2008 % To use this code, you should only prepare your cost function and apply % CCA to it. Please read the guide available in my home page. %% Problem Statement ProblemParams.CostFuncName = 'ICA_CostFunction_Fcn'; % You should state the name of your cost function here. ProblemParams.NPar = d*k; % Number of optimization variables of your objective function. "NPar" is the dimention of the optimization problem. ProblemParams.VarMin = ones(1,d*k) ; % Lower limit of the optimization parameters. You can state the limit in two ways. 1) 2), ProblemParams.VarMax = ones(1,d*k); % Lower limit of the optimization parameters. You can state the limit in two ways. 1) 2) CostFuncExtraParams.k = k; CostFuncExtraParams.d = d; CostFuncExtraParams.n = n; CostFuncExtraParams.data = data; ProblemParams.CostFuncExtraParams = CostFuncExtraParams; % Modifying the size of VarMin and VarMax to have a general form for i=1:d t=data(:,i); for j=0:k-1 ProblemParams.VarMin(1,i+(j*d)) =min(t,[],1) ; ProblemParams.VarMax(1,i+(j*d)) =max(t,[],1) ; end end ProblemParams.SearchSpaceSize = ProblemParams.VarMax - ProblemParams.VarMin; %% Algorithmic Parameter Setting AlgorithmParams.NumOfCountries = 100; % Number of initial countries. AlgorithmParams.NumOfInitialImperialists = 8; % Number of Initial Imperialists. AlgorithmParams.NumOfAllColonies = AlgorithmParams.NumOfCountries - AlgorithmParams.NumOfInitialImperialists; AlgorithmParams.NumOfDecades = 500; AlgorithmParams.RevolutionRate = 0.3; % Revolution is the process in which the socio-political characteristics of a country change suddenly. AlgorithmParams.AssimilationCoefficient = 5; % In the original paper assimilation coefficient is shown by "beta". AlgorithmParams.AssimilationAngleCoefficient = .5; % In the original paper assimilation angle coefficient is shown by "gama". AlgorithmParams.Zeta = 0.05; % Total Cost of Empire = Cost of Imperialist + Zeta * mean(Cost of All Colonies); AlgorithmParams.DampRatio = 0.99; AlgorithmParams.StopIfJustOneEmpire = false; % Use "true" to stop the algorithm when just one empire is remaining. Use "false" to continue the algorithm. AlgorithmParams.UnitingThreshold = 0.02; % The percent of Search Space Size, which enables the uniting process of two Empires. %% Display Setting DisplayParams.PlotEmpires = false; % "true" to plot. "false" to cancel ploting. if DisplayParams.PlotEmpires DisplayParams.EmpiresFigureHandle = figure('Name','Plot of Empires','NumberTitle','off'); DisplayParams.EmpiresAxisHandle = axes; end DisplayParams.PlotCost = false; % "true" to plot. "false" if DisplayParams.PlotCost DisplayParams.CostFigureHandle = figure('Name','Plot of Minimum and Mean Costs','NumberTitle','off'); DisplayParams.CostAxisHandle = axes; end ColorMatrix = [1 0 0 ; 0 1 0 ; 0 0 1 ; 1 1 0 ; 1 0 1 ; 0 1 1 ; 1 1 1 ; 0.5 0.5 0.5; 0 0.5 0.5 ; 0.5 0 0.5 ; 0.5 0.5 0 ; 0.5 0 0 ; 0 0.5 0 ; 0 0 0.5 ; 1 0.5 1 ; 0.1*[1 1 1]; 0.2*[1 1 1]; 0.3*[1 1 1]; 0.4*[1 1 1]; 0.5*[1 1 1]; 0.6*[1 1 1]]; DisplayParams.ColorMatrix = [ColorMatrix ; sqrt(ColorMatrix)]; DisplayParams.AxisMargin.Min = ProblemParams.VarMin; DisplayParams.AxisMargin.Max = ProblemParams.VarMax; %% Creation of Initial Empires InitialCountries = GenerateNewCountry(AlgorithmParams.NumOfCountries , ProblemParams); % Calculates the cost of each country. The less the cost is, the more is the power. %if isempty(ProblemParams.CostFuncExtraParams) % InitialCost = feval(ProblemParams.CostFuncName,InitialCountries); %else InitialCost = feval(ProblemParams.CostFuncName,InitialCountries,ProblemParams.CostFuncExtraParams); %end [InitialCost,SortInd] = sort(InitialCost); % Sort the cost in assending order. The best countries will be in higher places InitialCountries = InitialCountries(SortInd,:); % Sort the population with respect to their cost. Empires = CreateInitialEmpires(InitialCountries,InitialCost,AlgorithmParams, ProblemParams); %% Main Loop MinimumCost = repmat(nan,AlgorithmParams.NumOfDecades,1); MeanCost = repmat(nan,AlgorithmParams.NumOfDecades,1); if DisplayParams.PlotCost axes(DisplayParams.CostAxisHandle); if any(findall(0)==DisplayParams.CostFigureHandle) h_MinCostPlot=plot(MinimumCost,'r','LineWidth',1.5,'YDataSource','MinimumCost'); hold on; h_MeanCostPlot=plot(MeanCost,'k:','LineWidth',1.5,'YDataSource','MeanCost'); hold off; pause(0.05); end end for Decade = 1:AlgorithmParams.NumOfDecades AlgorithmParams.RevolutionRate = AlgorithmParams.DampRatio * AlgorithmParams.RevolutionRate; % Remained = AlgorithmParams.NumOfDecades - Decade for ii = 1:numel(Empires) %% Assimilation; Movement of Colonies Toward Imperialists (Assimilation Policy) Empires(ii) = AssimilateColonies(Empires(ii),AlgorithmParams,ProblemParams); %% Revolution; A Sudden Change in the Socio-Political Characteristics Empires(ii) = RevolveColonies(Empires(ii),AlgorithmParams,ProblemParams); %% New Cost Evaluation if isempty(ProblemParams.CostFuncExtraParams) Empires(ii).ColoniesCost = feval(ProblemParams.CostFuncName,Empires(ii).ColoniesPosition); else Empires(ii).ColoniesCost = feval(ProblemParams.CostFuncName,Empires(ii).ColoniesPosition,ProblemParams.CostFuncExtraParams); end %% Empire Possession (****** Power Possession, Empire Possession) Empires(ii) = PossesEmpire(Empires(ii)); %% Computation of Total Cost for Empires Empires(ii).TotalCost = Empires(ii).ImperialistCost + AlgorithmParams.Zeta * mean(Empires(ii).ColoniesCost); end %% Uniting Similiar Empires Empires = UniteSimilarEmpires(Empires,AlgorithmParams,ProblemParams); %% Imperialistic Competition Empires = ImperialisticCompetition(Empires); if numel(Empires) == 1 && AlgorithmParams.StopIfJustOneEmpire break end %% Displaying the Results DisplayEmpires(Empires,AlgorithmParams,ProblemParams,DisplayParams); ImerialistCosts = [Empires.ImperialistCost]; MinimumCost(Decade) = min(ImerialistCosts); MeanCost(Decade) = mean(ImerialistCosts); if DisplayParams.PlotCost refreshdata(h_MinCostPlot); refreshdata(h_MeanCostPlot); drawnow; pause(0.01); end end % End of Algorithm BestCost = MinimumCost(end); BestIndex = find(ImerialistCosts == min(ImerialistCosts)); BestIndex = BestIndex(1); BestSolution = Empires(BestIndex).ImperialistPosition;