Can anyboby help me to decode the following code and tell waht the code is actually doing?

%routine 1
%For feed-forward neural networks and genetics
clear all;
load file.mat

transfer_function=4; % 1 for log-sigmoid, 2 for tangent hyperbolic, 3 for radbas and 4 for linear
if transfer_function==1 % 1 for log-sigmoid, 2 for tangent hyperbolic, 3 for radbas and 4 for linear
transfer_delta_function=1;
elseif transfer_function==2
transfer_delta_function=2;
elseif transfer_function==3
transfer_delta_function=3;
elseif transfer_function==4
transfer_delta_function=4;
end
transfer_function_O=4; % 1 for log-sigmoid, 2 for tangent hyperbolic, 3 for radbas and 4 for linear
constant=0;
pc=0.2; % Crossover rate
pm=0.005; % Mutation rate
nforecast=7;

y=file(1:end-nforecast,2)
x=file(1:end-nforecast,3)
%y = file(1:28,1);
disp(y)
%x = file(1:28,2);
disp(x)
if constant == 0
%x=x;
elseif constant==1
x=[ones(nk,1) x];
end
[nk ni]=size(x);
n_outputs = 1;
n_inputs = ni;
num_hidden = ni;
chrom =num_hidden*n_inputs + num_hidden*n_outputs;
chrom =num_hidden*(n_inputs+1) + (num_hidden + 1)*n_outputs;
lb= min(min(x)); % Lower bound of the parameters to be optimized
ub=max(max(x)); %Upper bound of the parameters to be optimized
popsize=30;
chromlength=chrom;
Range=repmat((ub-lb),[popsize chrom]);
Lower = repmat(lb, [popsize chrom]);
pop=rand(popsize, chromlength).*Range+ Lower;
f=x;
for iterations =1:20
% chromosoms' fitness evaluation
[px,py] = size(pop);
Chromosome = pop;
for i = 1:px
for j = 1:num_hidden*ni
w1(j) = Chromosome (i,j);
end
w1 = reshape(w1,num_hidden,ni);
k=1;
for j = n_inputs*num_hidden+1:(num_hidden*ni + n_outputs*num_hidden)
w2(k)= Chromosome (i,j);
%w2=w2'
k=k+1;
end
[X Y]=meshgrid(w2);
w2=Y(:,1);
k=1;
for j = (num_hidden*n_inputs + n_outputs*num_hidden + 1):(num_hidden*n_inputs + n_outputs*num_hidden + num_hidden)input_hidden_Bias(k)= Chromosome (i,j);
    k=k+1;
end
k=1;
for j = (num_hidden*n_inputs + n_outputs*num_hidden + num_hidden + 1):(num_hidden*n_inputs + n_outputs*num_hidden + num_hidden +n_outputs)
hidden_output_Bias(k)= Chromosome (i,j);
k=k+1;
end
if transfer_function==1
h=logsig(f*w1');
elseif transfer_function==2
h=tansig(f*w1');
elseif transfer_function==3
h=radbas(f*w1');
elseif transfer_function==4
h=purelin(f*w1');
end
h1 = h;
if transfer_function_O==1;
y1=logsig(h1*w2);
elseif transfer_function_O==2;
y1=tansig(h1*w2);
elseif transfer_function_O==3;
y1=radbas(h1*w2);
elseif transfer_function_O==4;
y1=purelin(h1*w2);
end
err = (y-y1);
err = reshape(err,nk*n_outputs,1);
object_value(i)=1/2*mse(err);
end
fitvalue = object_value;
totalfit=sum(fitvalue);
fitvalue=fitvalue/totalfit;
fitvalue=cumsum(fitvalue);
[px,py]=size(pop);
ms=sort(rand(px,1));
fitin=1;
newin=1;
while newin<=px
if(ms(newin))<fitvalue(fitin)
newpop(newin,:)=pop(fitin,:);
newin=newin+1;
else
fitin=fitin+1;
end
end
% crossover between chromosoms
pop = newpop;
length_chrom = size(pop,2);
c_point = ceil(rand(size(pop,1)/2,1)*(length_chrom-1));
c_point = c_point.*(rand(size(c_point))<pc);
for i = 1:length(c_point);
newpop([2*i-1 2*i],:) = [pop([2*i-1 2*i],1:c_point(i)) pop([2*i
2*i-1],c_point(i)+1:length_chrom)];
end
% mutation of chromosoms
pop = newpop;
mutated = find(rand(size(pop))<pm);
newpop = pop;
newpop(mutated) = 1-pop(mutated);
% finding the best individual
pop = newpop;
bestindividual=pop(1,:);
bestfit=fitvalue(1);
for i=2:px
if fitvalue(i)<bestfit
bestindividual=pop(i,:);
bestfit=fitvalue(i);
end
end
best_gen=bestindividual;
for j = 1:num_hidden*ni
w1(j) = best_gen (1,j);
end
k=1;
for j = ni*num_hidden+1:(num_hidden*ni + n_outputs*num_hidden)
w2(k)= best_gen (1,j);
k=k+1;
end
iterations=iterations+1;

arraygbest ( iterations )= bestfit;
indexiter ( iterations ) = iterations;
end
%end
test_sample=file(29:35,1);
%test_sample=file(end-nforecast:end-1,2);
%test_y=file(end-nforecast+1:end,1);
test_y=file(29:35,2);
tt=length(test_sample);
if constant==0
test_sample=test_sample;
elseif constant==1
test_sample=[ones(size(test_y)) test_sample];
end
if transfer_function==1
yfore=logsig(test_sample*w1);
yfore=logsig(yfore*w2);
elseif transfer_function==2
yfore=tansig(test_sample*w1);
yfore=tansig(yfore*w2);
elseif transfer_function==3
yfore=radbas(test_sample*w1);
yfore=radbas(yfore*w2);
elseif transfer_function==4
yfore=test_sample*w1;
yfore=yfore*w2;
end
figure, plot(y,'-r'); hold on; plot(y1,'-b');
xlabel('Water activity')
ylabel('EMC')
%title('In_sample forecasts')
h1 = legend('Actual','forecasts',1);
figure, plot(test_y,'-r'); hold on; plot(yfore,'-b');
xlabel('Water activity')
ylabel('EMC')
%title('Out_of_sample forecasts')
h = legend('Actual','forecasts',1);
figure, plot (indexiter , test_y);
xlabel('epochs')
ylabel('Error')
title('Number of Epochs')

[Greg Heath]

No one will attempt to even read this unless you format it.

Search ANSWERS using

 how-to-format

[Greg Heath]

Now that it is formatted. It is still too painful to follow. Obviously it is a program that trains using a genetic algorithm. It's been more than 10 years for me. So I suggest:

1. Search the NEWSGROUP and ANSWERS for other genetic training NN code that is easier to understand.

2. Then, if needed, come back here.

3. Start with

 neural ga

and

 neural genetic

Hope this helps.

Greg

[Walter Roberson]

I wouldn't even consider answering this question without the reference to the paper that the code is from.