基于GMDH的数据分组处理方法mtalab仿真【包括程序操作视频】

clc; clear; close all; %% Input load fisheriris.mat; Input=reshape(meas,1,[]); % Preprocessing - convert matrix to vector Target(1:50)=1;Target(51:100)=2;Target(101:150)=3;Target=Target'; % Original labels classes= 3; % More value of "Variation" means more synthetic data. Variation = 6; for i=1:Variation Delays = [1 2 3 4]; [Inputs, Targets] = CreateTimeSeriesData(Input,Delays); nData = size(Inputs,2); Perm = randperm(nData); % Create GMDH Network params.MaxLayerNeurons = 30; % Maximum Number of Neurons in a Layer params.MaxLayers = 9; % Maximum Number of Layers params.alpha = 0.3; % Selection Pressure params.pTrain = 0.9; % Train Ratio % Train GMDH gmdh = GMDH(params, Inputs, Targets); % GMDH Model on Train Outputs{i} = ApplyGMDH(gmdh, Inputs); end % Converting cell to matrix for i = 1 : Variation Generated(:,i)=Outputs{i}; end % Converting matrix to cell P = size(Input); P = P (1,2); S = size(Outputs{i}); SO = size (meas); SF = SO (1,2); SO = SO (1,1); SS = S (1,2); R = SS*SO/P; for i = 1 : Variation Generated1{i}=reshape(Generated(:,i),[R,SF]); end % Converting cell to matrix (the last time) Synthetic = cell2mat(Generated1'); % K-means clustering to get the labels [idx,C] = kmeans(Synthetic,classes); %% Plot data and classes Feature1=1; Feature2=3; f1=meas(:,Feature1); % feature1 f2=meas(:,Feature2); % feature 2 ff1=Synthetic(:,Feature1); % feature1 ff2=Synthetic(:,Feature2); % feature 2 figure('units','normalized','outerposition',[0 0 1 1]) subplot(2,2,1) plot(meas, 'linewidth',1); title('Original Data'); ax = gca; ax.FontSize = 12; ax.FontWeight='bold'; grid on; subplot(2,2,2) plot(Synthetic, 'linewidth',1); title('Synthetic Data'); ax = gca; ax.FontSize = 12; ax.FontWeight='bold'; grid on; subplot(2,2,3) gscatter(f1,f2,Target,'rkgb','.',20); title('Original'); ax = gca; ax.FontSize = 12; ax.FontWeight='bold'; grid on; subplot(2,2,4) gscatter(ff1,ff2,idx,'rkgb','.',20); title('Synthetic'); ax = gca; ax.FontSize = 12; ax.FontWeight='bold'; grid on; %% Train and Test % Training Synthetic dataset by SVM Mdlsvm = fitcecoc(Synthetic,idx); CVMdlsvm = crossval(Mdlsvm); SVMError = kfoldLoss(CVMdlsvm); SVMAccAugTrain = (1 - SVMError)*100; % Predict new samples (the whole original dataset) [label5,score5,cost5] = predict(Mdlsvm,meas); sizlbl=R; % Test error and accuracy calculations a=0;b=0;c=0; for i=1:R if label5(i)== 1 a=a+1; elseif label5(i)==2 b=b+1; else label5(i)==3 c=c+1; end;end; erra=abs(a-50);errb=abs(b-50);errc=abs(c-50); err=erra+errb+errc;TestErr=err*100/R;SVMAccAugTest=100-TestErr; % Test Accuracy % Result SVM AugResSVM = [' Synthetic Train SVM "',num2str(SVMAccAugTrain),'" Synthetic Test SVM"', num2str(SVMAccAugTest),'"']; disp(AugResSVM);