基于遗传算法优化BP神经网络多输入多输出回归预测，GA-BP多输出回归预测 评价指标包括:R2、MAE、MSE、RMSE和M

function [Metrics]=polygonareametric(ActualLabel, PredictedLabel,isPlot) %%%%% WARNING %%%%%%% %NUMERICALLY LARGER CLASS WILL BE AUTOMATICALLY ASSIGNED AS PositiveClass %THIS IS BECAUSE OF THE FUNCTION OF perfcurve. IT REQUIRES LARGER CLASS AS PositiveClass. % INTRODUCTION: % This study proposes a stable and profound knowledge criterion that allows the performance of a classifier % to be evaluated with only a single metric called as polygon area metric (PAM). This function is not only % calculates PAM value, but also gives Classification Accuracy (CA), Sensitivity (SE), Specificity (SP), % Kappa (K) and F measure metrics. % % CITATION INFORMATION: % Please cite the following paper for the usage of PAM value: % Aydemir O., A New Performance Evaluation Metric for Classifiers: Polygon Area Metric, Journal of Classification, (2020). https://doi.org/10.1007/s00357-020-09362-5 % % USAGE OF THE FUNCTION: % INPUTS; % -ActualLabel: Actual label of the trials (samples), 1xN dimension binary labels % -PredictedLabel: Predicted (estimated) label of the trials (samples), 1xN dimension binary labels % -isPlot: A logical value indicating whether the resultant figure will be drawn. Default is true % % OUTPUT; % -Metrics: This struct gives 7 evaluation metrics which are Polygon Area... % (PA), Classification_Accuracy (CA), Sensitivity (SE), Specificity... % (SP), AUC (AUC), Kappa (K), F_measure (F_M), respectively. % AUC: Area under curve value, which should be obtained by Receiver operating characteristic (ROC), 0<AUC<1 % % EXAMPLE; % -ActualLabel=[1 1 1 1 1 0 0 0 0]; % -PredictedLabel=[1 1 1 0 0 0 0 0 1]; % -[Metrics]=polygonareametric(ActualLabel,PredictedLabel) %Code introduction if nargin<2 error('You have to supply all required input paremeters, which are ActualLabel, PredictedLabel') end if nargin < 3 isPlot = true; end %plotting the widest polygon A1=1; A2=1; A3=1; A4=1; A5=1; A6=1; a=[-A1 -A2/2 A3/2 A4 A5/2 -A6/2 -A1]; b=[0 -(A2*sqrt(3))/2 -(A3*sqrt(3))/2 0 (A5*sqrt(3))/2 (A6*sqrt(3))/2 0]; if isPlot figure plot(a, b, '--bo','LineWidth',1.3) axis([-1.5 1.5 -1.5 1.5]); set(gca,'FontName','Times New Roman','FontSize',12); hold on %grid end % Calculating the True positive (TP), False Negative (FN), False Positive... % (FP),True Negative (TN), Classification Accuracy (CA), Sensitivity (SE), Specificity (SP),... % Kappa (K) and F measure (F_M) metrics PositiveClass=max(ActualLabel); NegativeClass=min(ActualLabel); cp=classperf(ActualLabel,PredictedLabel,'Positive',PositiveClass,'Negative',NegativeClass); CM=cp.DiagnosticTable; TP=CM(1,1); FN=CM(2,1); FP=CM(1,2); TN=CM(2,2); CA=cp.CorrectRate; SE=cp.Sensitivity; %TP/(TP+FN) SP=cp.Specificity; %TN/(TN+FP) Pr=TP/(TP+FP); Re=TP/(TP+FN); F_M=2*Pr*Re/(Pr+Re); FPR=FP/(TN+FP); TPR=TP/(TP+FN); K=TP/(TP+FP+FN); [X1,Y1,T1,AUC] = perfcurve(ActualLabel,PredictedLabel,PositiveClass); %ActualLabel(1) means that the first class is assigned as positive class %plotting the calculated CA, SE, SP, AUC, K and F_M on polygon x=[-CA -SE/2 SP/2 AUC K/2 -F_M/2 -CA]; y=[0 -(SE*sqrt(3))/2 -(SP*sqrt(3))/2 0 (K*sqrt(3))/2 (F_M*sqrt(3))/2 0]; if isPlot plot(x, y, '-ko','LineWidth',1) set(gca,'FontName','Times New Roman','FontSize',12); % shadowFill(x,y,pi/4,80) fill(x, y,[0.8706 0.9216 0.9804]) end %calculating the PAM value % Get the number of vertices n = length(x); % Initialize the area p_area = 0; % Apply the formula for i = 1 : n-1 p_area = p_area + (x(i) + x(i+1)) * (y(i) - y(i+1)); end p_area = abs(p_area)/2; %Normalization of the polygon area to one. PA=p_area/2.59807; if isPlot %Plotting the Polygon plot(0,0,'r+') plot([0 -A1],[0 0] ,'--ko') text(-A1-0.3, 0,'CA','FontWeight','bold','FontName','Times New Roman') plot([0 -A2/2],[0 -(A2*sqrt(3))/2] ,'--ko') text(-0.59,-1.05,'SE','FontWeight','bold','FontName','Times New Roman') plot([0 A3/2],[0 -(A3*sqrt(3))/2] ,'--ko') text(0.5, -1.05,'SP','FontWeight','bold','FontName','Times New Roman') plot([0 A4],[0 0] ,'--ko') text(A4+0.08, 0,'AUC','FontWeight','bold','FontName','Times New Roman') plot([0 A5/2],[0 (A5*sqrt(3))/2] ,'--ko') text(0.5, 1.05,'J','FontWeight','bold','FontName','Times New Roman') plot([0 -A6/2],[0 (A6*sqrt(3))/2] ,'--ko') text(-0.65, 1.05,'FM','FontWeight','bold','FontName','Times New Roman') set(gca,'FontName','Times New Roman','FontSize',12); grid daspect([1 1 1]) end Metrics.PA=PA; Metrics.CA=CA; Metrics.SE=SE; Metrics.SP=SP; Metrics.AUC=AUC; Metrics.K=K; Metrics.F_M=F_M; categories = {'多边形面积PAM';'分类准确率';'灵敏度';'特异性';'曲线下面积AUC';'Kappa系数'; 'F_measure'}; printVar = cell(7,2); printVar(:,1)=categories; printVar(:,2)={PA, CA, SE, SP, AUC, K, F_M}; disp('预测结果打印:') for i=1:length(categories) fprintf('%23s: %.2f \n', printVar{i,1}, printVar{i,2}) end