130
else
caption=C(1);
end
if skip
C=C(skip+1:end);
end
%2.Accumulate info on the delimiter locations
P=cell(size(C)); %location
N=zeros(numel(P),1); %number
for ii=1:numel(C)
if ~rem(ii,10000)
display(horzcat('de-locations recognition:: iteration ',num2str(ii),' from
',num2str(numel(C))))
end
P{ii}=regexp(C{ii},delimiter); %tab: delimiter='\t'
N(ii)=numel(P{ii});
end
N=int32(N);
if (max(N)-min(N))>0
warning('---> csvInterp: *.csv corrupted')
end
%2.Assign table
X=cell(numel(C),m+1); %as it previously defined in <caption>
for ii=1:numel(C)
if ~rem(ii,10000)
display(horzcat('|table| assigning:: iteration ',num2str(ii),' from
',num2str(numel(C))))
end
%2.2.Slice every single string
p=P{ii};
131
m=numel(p);
current_string=cell(1,m+1);
if m
current_string{1}=C{ii}(1:p(1)-1);
for jj=1:m-1
current_string{jj+1}=C{ii}(p(jj)+L:p(jj+1)-1);
end
current_string{m+1}=C{ii}(p(m)+L:end);
else
current_string=C(ii);
end
X(ii,1:m+1)=current_string;
end
function S = quasiURLread (FileName,Encoding)
%-------------------------------------------
% usually, Encoding = 'windows-1251'
%-------------------------------------------
%1)prepare cell array for strings
% ... it will be truncated when <eof> will be reached
S=cell(100000000,1);
MASK=false(size(S));
p=0;
FID=fopen(FileName,'r','n',Encoding);
while ~feof(FID)
if ~rem(p,10000)
disp(horzcat('read:: iteration ',num2str(p),' from
<UNKNOWN_N._OF_LINES>'))
end
p=p+1;
132
S{p}=fgetl(FID);
MASK(p)=true;
end
fclose(FID);
S=S(MASK);
clear all
clc
% DO NOTHING; CLOSE; OPEN LONG; OPEN SHORT
load PR0.mat
load PRC.mat
load PROL.mat
load PROS.mat
X = [PR0 [ones(size(PR0, 1), 1) zeros(size(PR0, 1), 1) zeros(size(PR0, 1), 1)
zeros(size(PR0, 1), 1)]; ...
PRC [zeros(size(PRC, 1), 1) ones(size(PRC, 1), 1) zeros(size(PRC, 1), 1)
zeros(size(PRC, 1), 1)]; ...
PROL [zeros(size(PROL, 1), 1) zeros(size(PROL, 1), 1) ones(size(PROL,
1), 1) zeros(size(PROL, 1), 1)]; ...
PROS [zeros(size(PROS, 1), 1) zeros(size(PROS, 1), 1) zeros(size(PROS,
1), 1) ones(size(PROS, 1), 1)]];
for k = 1 : size(X, 1)
X(k, 1 : 30) = X(k, 1 : 30) - mean(X(k, 1 : 30));
end
NN = perceptron1_(X, 400, 80);
Приложение 15
133
function f = perceptron1_ (a1, a2, a3)
action=isobject(a2);
switch action
case false
PR=a1;
Q=a2;
n0=a3;
X=PR(:,1:end-4);
Y=PR(:,end-4+1:end);
f=ddrstandartnet_core(X,Y,n0,Q);
case true
X=a1;
NET=a2;
f=ddrstandartnet_apply(NET,X);
end
function NN = ddrstandartnet_core (X, Y, n0, Q)
PR=[X'; Y'];
NN=feedforwardnet([n0 ceil(n0/2) n0],'trainrp');
NN.layers{1}.transferFcn='tansig';
NN.layers{2}.transferFcn='tansig';
NN.layers{3}.transferFcn='tansig';
NN.layers{4}.transferFcn='tansig';
NN.trainparam.max_fail=Q;
NN.trainparam.showWindow=true;
NN.trainparam.epochs=1e8;
NN=train(NN,PR(1:end-4,:),PR(end-4+1:end,:),'useParallel','no');
function f = ddrstandartnet_apply (NET, X)
f=sim(NET,X');