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Copy pathZero_FPR_exe.m
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Zero_FPR_exe.m
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%% Example of ZeroFPR_SVDD
% This script shows how the zeroFPR_SVDD algorithm works.
% Given a training set X labelled in a target class (+1)
% and in a negative class (-1), the algorithm performs a
% cleaning of the SVDD classification until a threshold on
% the percentage of False Positives (FP) is reached.
clc; clear all; close all; %#ok<CLALL>
% Create the dataset
N1 = 1000; % number of target points
N2 = 100; % number of negative points
X1 = MixGauss([1;1],[1,1],N1); % target class
X2 = MixGauss([1;1],[2,2],N2); % negative class
Xtr = [X1; X2];
Y1 = ones(N1,1);
Y2 = -ones(N2,1);
Ytr = [Y1; Y2];
ir = randperm(size(Xtr, 1));
Xtr = Xtr(ir',:);
Ytr = Ytr(ir');
%%%%%%%%%%%%%% XXXX %%%%%%%%%%%%%%%%%%%%%%
figure(1)
gscatter(Xtr(:,1), Xtr(:,2), Ytr, 'br'); % display the data
%%%%%%%%%%%%%% XXXX %%%%%%%%%%%%%%%%%%%%%%
% Classification via SVDD
N1 = nnz(Ytr(:,1)==+1);
N2 = nnz(Ytr(:,1)==-1);
C1 = 0.5;
C2 = 1; % if Kernel is linear, choose C2 = 1/N2;
kernel='gaussian';
param=1;
[alpha, Rsquared,a,SV,YSV]= ...
SVDD_N1C_TRAINING(Xtr, Ytr, kernel, C1, C2, param,'on');
%%%%%%%%%%%%%% XXXX %%%%%%%%%%%%%%%%%%%%%%
% Display the classification
plotSVDD(Xtr, Ytr, Xtr, Ytr, SV, YSV, kernel, param, alpha, Rsquared, a, 2);
%%%%%%%%%%%%%% XXXX %%%%%%%%%%%%%%%%%%%%%%
% Display the metrics
y = SVDD_N1C_TEST(Xtr, Ytr, alpha, Xtr, kernel, param, Rsquared);
P = nnz(Ytr(:,1)==+1);
N = nnz(Ytr(:,1)==-1);
Y = [y Ytr];
TN = sum(Y(:,1)==-1 & Y(:,2)==-1);
FN = sum(Y(:,1)==-1 & Y(:,2)==+1);
TP = sum(Y(:,1)==+1 & Y(:,2)==+1);
FP = sum(Y(:,1)==+1 & Y(:,2)==-1);
FNR_start = FN/P;
FPR_start = FP/N;
ACC_start = (TP+TN)/(P+N);
F1_start = 2*TP/(2*TP+FP+FN);
PPV_start = TP/(TP+FP);
NPV_start = TN/(TN+FN);
TotalN_start = TP+FP;
%figure(3)
%cm = confusionchart(Ytr, y);
%%%%%%%%%%%%%% XXXX %%%%%%%%%%%%%%%%%%%%%%
% zeroFPR_SVDD algorithm
treshold = 0.3; % treshold
[X_star, Y_star, alpha_star, Rsquared_star, ...
a_star, SV_star, YSV_star, param_star] = ...
ZeroFPR_SVDD(Xtr, Ytr, alpha, Rsquared, kernel, param, C1, C2, treshold, 'Y');
%%%%%%%%%%%%%% XXXX %%%%%%%%%%%%%%%%%%%%%%
% Display the classification after the algorithm cleaning
plotSVDD(X_star, Y_star, Xtr, Ytr, SV_star, YSV_star, kernel, param_star, alpha_star, Rsquared_star,a_star,4);
%%%%%%%%%%%%%% XXXX %%%%%%%%%%%%%%%%%%%%%%
% Display the metrics
y = SVDD_N1C_TEST(X_star, Y_star, alpha_star, Xtr, kernel, param_star, Rsquared_star);
P = nnz(Ytr(:,1)==+1);
N = nnz(Ytr(:,1)==-1);
Y = [y Ytr];
TN = sum(Y(:,1)==-1 & Y(:,2)==-1);
FN = sum(Y(:,1)==-1 & Y(:,2)==+1);
TP = sum(Y(:,1)==+1 & Y(:,2)==+1);
FP = sum(Y(:,1)==+1 & Y(:,2)==-1);
FNR_end = FN/P;
FPR_end = FP/N;
ACC_end = (TP+TN)/(P+N);
F1_end = 2*TP/(2*TP+FP+FN);
PPV_end = TP/(TP+FP);
NPV_end = TN/(TN+FN);
TotalN_end = TP+FP;
%figure(5)
%cm = confusionchart(Ytr, y);