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mainDE.asv
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mainDE.asv
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clear all
clc
%addpath('C:\Users\WYF\Desktop\SIMLR-SIMLR\MATLAB\src')
% identify all input arguments
rand('state', 0);
%%%% for Four-Gaussian dataset %%%%%
for F=0.4:0.1:1
for CR=0.1:0.1:1
RRR=[];
problemSet = [1 : 6];
for problemIndex = 1:6
problem = problemSet(problemIndex)
switch problem
case 1
Data= importdata('Data_Buettner.mat') %import Four-Gaussian data
X=Data.in_X;
case 2
Data= importdata('Data_Deng.mat') %import Four-Gaussian data
X=Data.in_X;
case 3
Data= importdata('Data_Ginhoux.mat') %import Four-Gaussian data
X=Data.in_X;
case 4
Data= importdata('Data_Pollen.mat') %import Four-Gaussian data
X=Data.in_X;
case 5
Data= importdata('Data_Ting.mat') %import Four-Gaussian data
X=Data.in_X;
case 6
Data= importdata('Data_Treutlin.mat') %import Four-Gaussian data
X=Data.in_X;
end
X1=normalizeData(X);
K = length(unique(Data.true_labs)) % the number of clusters in the final clustering (using in consensus functions)
truelabels = Data.true_labs; %import Four-Gaussian truelabels
%%%%%%%%%%%%%%%%%%%%%%%%%%%dimensional reduction+kmeans
vPredictClass=[];
for i = 2: 20
option.algorithm='nmfrule';
[A,Y]=nmf(X',i,option);
Cl = kmeans(Y',K);
while length(unique(Cl)) ~= K;
Cl = kmeans(Y',K);
end
vPredictClass = [vPredictClass Cl];
end
D=size(vPredictClass,2);
M=3;
p1 = [99 13 7 5 4 3 3 2 3];
p2 = [ 0 0 0 0 1 2 2 2 2];
p1 = p1(M-1);
p2 = p2(M-1);
[N,W] = F_weight(p1,p2,M);
W(W==0) = 0.000001;
T = 4;
B = zeros(N);
for i = 1 : N
for j = i : N
B(i,j) = norm(W(i,:)-W(j,:));
B(j,i) = B(i,j);
end
end
[~,B] = sort(B,2);
B = B(:,1:T);
MaxFES = 1000;
Population = rand(N,D);
Population1 = Population>0.5;
Dim=D+2;
Para=[ randi([1,3],1,N)' randi([1,3],1,N)'];
for i=1:N
Index=find(Population1(i,:)==1);
if size(Index,2)==0
Index=1;
end
[FunctionValue(i,1:2), POP_label(i,:)] =objectivefunction(vPredictClass(:,Index),Para(i,:),K,truelabels,X1);
FunctionValue(i,3) =sum(Population1(i,:));
end
Z = min(FunctionValue);
Coding='Binary';
Boundary=[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1; 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0];
Generations=10;
nr = 2;
for Gene = 1 : Generations
%Fmax = max(FunctionValue);
%Fmin = Z;
%FunctionValue = (FunctionValue-repmat(Fmin,N,1))./repmat(Fmax-Fmin,N,1);
for i = 1 : N
P = 1:N;
k = randperm(length(P));
Offspring = F_generator(Population(P(k(1)),:),Population(P(k(2)),:),Population(P(k(3)),:),Boundary, F, CR);
Offspring1=Offspring>0.5;
Index=find(Offspring1==1);
if size(Index,2)==0
Index=1;
end
[OffFunValue(1:2),Off_label] =objectivefunction(vPredictClass(:,Index),Para(i,:),K,truelabels,X1);
OffFunValue(3) =sum(Offspring1);
Z = min(Z,OffFunValue);
%OffFunValue = (OffFunValue-Fmin)./(Fmax-Fmin);
for j = 1 : T
g_old = max(abs(FunctionValue(B(i,j),:)-Z).*W(B(i,j),:));
g_new = max(abs(OffFunValue-Z).*W(B(i,j),:));
if g_new < g_old
%更新当前向量的个体
Population(B(i,j),:) = Offspring;
FunctionValue(B(i,j),:) = OffFunValue;
POP_label(B(i,j),:)=Off_label;
else
Para(B(i,j),:)=[ randi([1,3],1,1)' randi([1,3],1,1)'];
end
end
%反归一化
%FunctionValue = FunctionValue.*repmat(Fmax-Fmin,N,1)+repmat(Fmin,N,1);
end
end
for i=1:1:N
NNMI(i,:) = Cal_NMI(POP_label(i,:),truelabels);
[ARAR(i,:),RI,MI,HI]=RandIndex(POP_label(i,:),truelabels);
end
Results(problemIndex,:)=max(NNMI)
Results1(problemIndex,:)=max(ARAR)
end
RRR=[Results' Results1'];
end
end
Results