一 基于Bp算法的人脸识别专题 pcabp kpcabp算法算法

function ROCout=roc(varargin) % ROC - Receiver Operating Characteristics. % The ROC graphs are a useful tecnique for organizing classifiers and % visualizing their performance. ROC graphs are commonly used in medical % decision making. % If you have not downloaded partest % http://www.mathworks.com/matlabcentral/fileexchange/12705 % and L5P % http://www.mathworks.com/matlabcentral/fileexchange/38043 % the routine will do and compute several data on test performance. % % Syntax: ROCout=roc(x,thresholds,alpha,verbose) % % Input: x - This is a Nx2 data matrix. The first column is the column of the data value; % The second column is the column of the tag: unhealthy (1) and % healthy (0). % Thresholds - If you want to use all unique values in x(:,1) % then set this variable to 0 or leave it empty; % else set how many unique values you want to use (min=3); % alpha - significance level (default 0.05) % verbose - if you want to see all reports and plots (0-no; 1-yes by % default); % % Output: if verbose = 1 % the ROCplots, the sensitivity and specificity at thresholds; the Area % under the curve with Standard error and Confidence interval and % comment, Cut-off point for best sensitivity and specificity. % (Optional) the test performances at cut-off point. % if ROCout is declared, you will have a struct: % ROCout.AUC=Area under the curve (AUC); % ROCout.SE=Standard error of the area; % ROCout.ci=Confidence interval of the AUC % ROCout.co=Cut off points % ROCdata.xr and ROCdata.yr points for ROC plot % % USING roc WITHOUT ANY DATA, IT WILL RUN A DEMO % % Created by Giuseppe Cardillo % [email protected] % % To cite this file, this would be an appropriate format: % Cardillo G. (2008) ROC curve: compute a Receiver Operating Characteristics curve. % http://www.mathworks.com/matlabcentral/fileexchange/19950 %Input Error handling args=cell(varargin); nu=numel(args); if isempty(nu) error('Warning: almost the data matrix is required') elseif nu>4 error('Warning: Max four input data are required') end default.values = {[165 1;140 1;154 1;139 1;134 1;154 1;120 1;133 1;150 1;... 146 1;140 1;114 1;128 1;131 1;116 1;128 1;122 1;129 1;145 1;117 1;140 1;... 149 1;116 1;147 1;125 1;149 1;129 1;157 1;144 1;123 1;107 1;129 1;152 1;... 164 1;134 1;120 1;148 1;151 1;149 1;138 1;159 1;169 1;137 1;151 1;141 1;... 145 1;135 1;135 1;153 1;125 1;159 1;148 1;142 1;130 1;111 1;140 1;136 1;... 142 1;139 1;137 1;187 1;154 1;151 1;149 1;148 1;157 1;159 1;143 1;124 1;... 141 1;114 1;136 1;110 1;129 1;145 1;132 1;125 1;149 1;146 1;138 1;151 1;... 147 1;154 1;147 1;158 1;156 1;156 1;128 1;151 1;138 1;193 1;131 1;127 1;... 129 1;120 1;159 1;147 1;159 1;156 1;143 1;149 1;160 1;126 1;136 1;150 1;... 136 1;151 1;140 1;145 1;140 1;134 1;140 1;138 1;144 1;140 1;140 1;159 0;... 136 0;149 0;156 0;191 0;169 0;194 0;182 0;163 0;152 0;145 0;176 0;122 0;... 141 0;172 0;162 0;165 0;184 0;239 0;178 0;178 0;164 0;185 0;154 0;164 0;... 140 0;207 0;214 0;165 0;183 0;218 0;142 0;161 0;168 0;181 0;162 0;166 0;... 150 0;205 0;163 0;166 0;176 0;],0,0.05,1}; default.values(1:nu) = args; [x threshold alpha verbose] = deal(default.values{:}); if isvector(x) error('Warning: X must be a matrix') end if ~all(isfinite(x(:))) || ~all(isnumeric(x(:))) error('Warning: all X values must be numeric and finite') end x(:,2)=logical(x(:,2)); if all(x(:,2)==0) error('Warning: there are only healthy subjects!') end if all(x(:,2)==1) error('Warning: there are only unhealthy subjects!') end if nu>=2 if isempty(threshold) threshold=0; else if ~isscalar(threshold) || ~isnumeric(threshold) || ~isfinite(threshold) error('Warning: it is required a numeric, finite and scalar THRESHOLD value.'); end if threshold ~= 0 && threshold <3 error('Warning: Threshold must be 0 if you want to use all unique points or >=2.') end end if nu>=3 if isempty(alpha) alpha=0.05; else3 if ~isscalar(alpha) || ~isnumeric(alpha) || ~isfinite(alpha) error('Warning: it is required a numeric, finite and scalar ALPHA value.'); end if alpha <= 0 || alpha >= 1 %check if alpha is between 0 and 1 error('Warning: ALPHA must be comprised between 0 and 1.') end end end if nu==4 verbose=logical(verbose); end end clear args default nu tr=repmat('-',1,80); lu=length(x(x(:,2)==1)); %number of unhealthy subjects lh=length(x(x(:,2)==0)); %number of healthy subjects z=sortrows(x,1); if threshold==0 labels=unique(z(:,1));%find unique values in z else K=linspace(0,1,threshold+1); K(1)=[]; labels=quantile(unique(z(:,1)),K)'; end labels(end+1)=labels(end)+1; ll=length(labels); %count unique value a=zeros(ll,2); b=a; c=zeros(ll,1);%array preallocation ubar=mean(x(x(:,2)==1),1); %unhealthy mean value hbar=mean(x(x(:,2)==0),1); %healthy mean value for K=1:ll if hbar<ubar TP=length(x(x(:,2)==1 & x(:,1)>labels(K))); FP=length(x(x(:,2)==0 & x(:,1)>labels(K))); FN=length(x(x(:,2)==1 & x(:,1)<=labels(K))); TN=length(x(x(:,2)==0 & x(:,1)<=labels(K))); else TP=length(x(x(:,2)==1 & x(:,1)<labels(K))); FP=length(x(x(:,2)==0 & x(:,1)<labels(K))); FN=length(x(x(:,2)==1 & x(:,1)>=labels(K))); TN=length(x(x(:,2)==0 & x(:,1)>=labels(K))); end M=[TP FP;FN TN]; a(K,:)=diag(M)'./sum(M); %Sensitivity and Specificity b(K,:)=diag(M)./sum(M,2); %Positive and Negative predictivity c(K)=trace(M)/sum(M(:)); %Efficiency end if hbar<ubar xroc=flipud(1-a(:,2)); yroc=flipud(a(:,1)); %ROC points labels=flipud(labels); else xroc=1-a(:,2); yroc=a(:,1); %ROC points end st=[1 mean(xroc) 1]; L=[0 0 0]; U=[Inf 1 Inf]; fo_ = fitoptions('method','NonlinearLeastSquares','Lower',L,'Upper',U,'Startpoint',st); ft_ = fittype('1-1/((1+(x/C)^B)^E)',... 'dependent',{'y'},'independent',{'x'},... 'coefficients',{'B', 'C', 'E'}); cfit = fit(xroc,yroc,ft_,fo_); xfit=linspace(0,1,500); yfit=feval(cfit,xfit); Area=trapz(xfit,yfit); %estimate the area under the curve %standard error of area Area2=Area^2; Q1=Area/(2-Area); Q2=2*Area2/(1+Area); V=(Area*(1-Area)+(lu-1)*(Q1-Area2)+(lh-1)*(Q2-Area2))/(lu*lh); Serror=realsqrt(V); %confidence interval ci=Area+[-1 1].*(realsqrt(2)*erfcinv(alpha)*Serror); if ci(1)<0; ci(1)=0; end if ci(2)>1; ci(2)=1; end m=zeros(1,4); if verbose %z-test SAUC=(Area-0.5)/Serror; %standardized area p=1-0.5*erfc(-SAUC/realsqrt(2)); %p-value %Performance of the classifier if Area==1 str='Perfect test'; elseif Area>=0.90 && Area<1 str='Excellent test'; elseif Area>=0.80 && Area<0.90 str='Good test'; elseif Area>=0.70 && Area<0.80 str='Fair test'; elseif Area>=0.60 && Area<0.70 str='Poor test'; elseif Area>=0.50 && Area<0.60 str='Fail test'; else str='Failed test - less than chance'; end %display results disp('ROC CURVE DATA') disp(tr) fprintf('Cut-off point\t\tSensivity\tSpecificity\tEfficiency\tPos. Pred.\tNeg. Pred.\n') table=[labels'; a(:,1)'; a(:,2)';c'; b(:,1)'; b(:,2)';]'; fprintf('%0.4f\t\t%0.4f\t\t%0.4f\t\t%0.4f\t\t%0.4f\t\t%0.4f\n',table') disp(tr) disp(' ') disp('ROC CURVE ANALYSIS') disp(' ') disp(tr) str2=['AUC\t\t\tS.E.\t\t\t\t' num2str((1-alpha)*100) '%% C.I.\t\t\tComment\n']; fprintf(str2) disp(tr) fprintf('%0.5f\t\t\t%0.5f\t\t\t%0.5f\t\t%0.5f\t\t\t%s\n',Area,Serror,ci,str) disp(tr)