# Can someone explain to me line by line whats happening in this code? Im REALLY in need of help.

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Commented: Walter Roberson on 7 Nov 2021 at 20:44
% Updated to Matlab2015
% MT,JV march 2013
% MC sept13, feb 2016
%% OPTIONS
clear
close all
i_hi=1; %0 NO /1 YES: HISTOGRAM FUNCTIONS
i_scplot=1; %0 NO /1 YES: scatterplot of features
i_roc=1; %0 NO /1 YES: ROC computation
%% Parameter initialitation
% SIGNAL TO NOISE RATIO (dB) AND INTER-CLASSES DISTANCE
disp(' ')
SNR=input('SNR (dB) = ');
dist=1; % Distance between classes mean ;
n_classes=2;
n_samples=[1000;1000];
n_feat=3;
M_Means=0.5*dist*[1,1,1;-1,-1,-1]/sqrt(n_feat); %Matrix containing two Mean vector
% Energy computation
Energy=0;
for i_classes=1:n_classes
V=squeeze(M_Means(i_classes,:));
Energy=Energy+V*V';
end
Energy=Energy/n_classes;
%noise variance computation
SNR=10^(SNR/10);
sig=Energy/SNR;
sig=sig/n_feat;
clear V Energy
%Covariance matrix
M_covar=zeros(n_feat,n_feat,n_classes);
sigma=sig*[1 1];
clear sig
for i_clase=1:n_classes
M_covar(:,:,i_clase)=sigma(i_clase)*eye(n_feat); %Covariance Matrix clase i_clase
end
%% Dataset generation
X=[];
Labels=[];
for i_class=1:n_classes
X=[X;mvnrnd(M_Means(i_class,:),M_covar(:,:,i_class),n_samples(i_class))];
Labels=[Labels; (i_class-1)*ones(n_samples(i_class),1)];
end
clear i_class
%% HISTOGRAMS
if i_hi==1
figure('name','HISTOGRAMS')
index= Labels==0;
for i_feat=1:n_feat
subplot(3,2,2*i_feat-1)
histfit(X(index,i_feat))
grid
zoom on
ylabel(['Feat ',num2str(i_feat)]);
title('Class 0');
end
index= Labels==1;
for i_feat=1:n_feat
subplot(3,2,2*i_feat)
histfit(X(index,i_feat))
grid
zoom on
ylabel(['Feat ',num2str(i_feat)]);
title('Class 1')
end
clear index i_feat
end
%% SCATTER PLOT
if i_scplot==1
varNames = {'feat 1' 'feat 2' 'feat 3'};
figure('name','Scatter Plot')
V=randperm(length(Labels));
gplotmatrix(X(V,:),X(V,:),Labels(V),'br','.',[],'on','hist',varNames,varNames)
grid
zoom on
% Plot en 3D
figure('name','Plot 3D clusters')
index=find(Labels==1);
plot3(X(index,1),X(index,2),X(index,3),'b+');
hold on
index=find(Labels==0);
plot3(X(index,1),X(index,2),X(index,3),'r*');
grid
clear index V varNames
end
%% Create a default (linear) discriminant analysis classifier:
linclass = fitcdiscr(X,Labels)
[Linear_out, Score_linear]= predict(linclass,X);
Linear_Pe=sum(Labels ~= Linear_out)/length(Labels);
fprintf(1,' error Linear = %g \n', Linear_Pe)
%% Create a quadratic discriminant analysis classifier:
%% ROC & CONFUSION MATRIX
if i_roc==1
figure
CM_Lineal=confusionmat(Labels,Linear_out)
end
% d(I) = (Y(I,:)-mu)*inv(SIGMA)*(Y(I,:)-mu)'
% d = mahal(Y,X)
Ind_class0= Labels==0;
Data_class0=X(Ind_class0,:);
Ind_class1= Labels==1;
Data_class1=X(Ind_class1,:);
dist=mahal(Data_class0,Data_class1);
d_01=mean(dist)
dist=mahal(Data_class1,Data_class0);
d_10=mean(dist)
clear Ind_class1 Data_class1 Ind_class0 Data_class0 dist
##### 2 CommentsShowHide 1 older comment
Walter Roberson on 7 Nov 2021 at 20:44
You flagged your own question as Unclear. As the person who posted the question, you should be the person who clarifies the question so that it is no longer unclear.

Steven Lord on 13 Oct 2021
If you mean what each of the commands does, you'd probably get faster answers if you looked at the documentation for the functions of whose purpose you're unsure. Type doc followed by the name of the command to open the documentation page.
If you mean the purpose of each command in the algorithm that this code implements, if they're available you'd probably want to ask to the person or people who wrote the code. If they're not available work through the code, line by line, and add comments explaining (in English or another human language, not in code) what that line is doing in your own words. When you're finished try reading through your comments in order and see if you now know the purpose of this script.