%%%%%script of c_OS.m

function OS=c_OS(tree,p,method)
  M=(tree,p,method);
  [p1,p2]=find_children(tree,p);
  M1=(tree,p1,method);
  M2=(tree,p2,method);
  proba_pass=length(tree.D(p1).n_l)/length(tree.D(p).n_l);
  proba_fail=length(tree.D(p2).n_l)/length(tree.D(p).n_l);
  OS=M-proba_pass*M1-proba_fail*M2;
end


function M=(tree,p,method)
  n_l=tree.D(p).n_l;
  S=load('training.mat');
  Y=S.Y; 
  y_p=Y(n_l);
  labels=unique(y_p);
  count=zeros(1,length(labels));
  for l=1:length(labels)
    count(l)=sum(Y==labels(l));
  end
  M=c_M(count/sum(count),method);
end
 
function [p1,p2]=find_children(tree,p)
  ind=find(tree.T==p);
  p1=ind(1); p2=ind(2);
end

function M=c_M(proba,method)
  if strcmp(method,'entropy')
    M=c_H(proba);
  else 
    M=c_GI(proba);
  end
end
 
function H=c_H(proba)
  H=sum(-proba.*log2(proba));
end

function GI=c_GI(proba)
  GI=1-sum(proba.^2);
end


%%%%%script of PNF_train3.m
function info=PNF_train3(method,displaying_function)
  [tree,p]=PNF_createRoot();
  OS=c_OS(tree,p,method);
  tree.D(p).OS=OS;
  while (1)
    p=find_node_for_split(tree);
    if OS_max<0 info.tree=tree; return; end
    [tree,p1,p2]=add2nodes(tree,p);
    OS1=c_OS(tree,p1,method);
    tree.D(p1).OS=OS1;
    tree.D(p1).c=c_class(tree.D(p1).n_l);
    OS2=c_OS(tree,p2,method);
    tree.D(p2).OS=OS2;
    tree.D(p2).c=c_class(tree.D(p2).n_l);
    info.tree=tree;
    displaying_function(info);
  end
end

function c=c_class(n_l)
  Y=S.Y(n_l);
  labels=unique(Y);
  count=zeros(1,length(labels));
  for l=1:length(labels)
    count(l)=sum(Y==labels(l));
  end
  [val,c_]=max(count);
  c=labels(c_);
end

function [p_best,OS_max]=find_node_for_split(tree)
  OS_max=-Inf;
  p_best;
  for p=1:length(tree.T)
    if tree.D(p).isleaf   
      OS=tree.D(p).OS;
      if OS>OS_max
        OS_max=OS; 
        p_best=p;
      end
    end
  end
end

function displaying_function(info)
  %figure(1); treeplot(tree.T);
  global tab_OA_training;
  global tab_OA_testing; 
  global tab_counter; 
  if isempty(tab_OA_training) tab_OA_training=[]; 
  if isempty(tab_OA_testing) tab_OA_testing=[]; 
  if isempty(tab_counter) tab_counter=0;
  tab_counter=tab_counter+1; 
  score_training=PNF_score1(info,'training',@PNF_predict1);
  score_testing=PNF_score1(info,'testing',@PNF_predict1);
  tab_OA_training(tab_counter)=score_training.overall_accuracy;
  tab_OA_testing(tab_counter)=score_testing.overall_accuracy;
  figure(1); plot(1:length(tab_OA_training),tab_OA_training,'b',1:length(tab_OA_testing),tab_OA_testing,'r');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
imHI=load('specialX.png');
imHI=imread('specialX.png');
imGT=imread('specialY.png');
figure(1); imshow(imHI)
figure(2); imshow(imGT)
X=zeros(size(imHI,1)*size(imHT,2),2);
X=zeros(size(imHI,1)*size(imHI,2),2);
Y=zeros(size(imHI,1)*size(imHI,2),1);
X(:,1)=reshape(imHI(:,:,1)',size(imHI,1)*size(imHT,2),1);
X(:,1)=reshape(imHI(:,:,1)',size(imHI,1)*size(imHI,2),1);
X(:,2)=reshape(imHI(:,:,2)',size(imHI,1)*size(imHI,2),1);
Y=reshape(imGT(:,:)',size(imHI,1)*size(imHI,2),1);
figure(2); plot(X(Y==0,1),X(Y==0,2),'b.')
figure(2); plot(X(Y==0,1),X(Y==0,2),'b.',X(Y==1,1),X(Y==1,2),'r.')
sum(Y==1)
figure(2); plot(X(Y==0,1),X(Y==0,2),'b.',X(Y>0,1),X(Y>0,2),'r.')
figure(3); plot(X(Y==0,1),X(Y==0,2),'b.',X(Y>0,1),X(Y>0,2),'r.')
figure(1); imshow(imHI)
figure(2); imshow(imGT)
Xt=zeros(size(X));
Xt(:,1)=sin(X(:,1)/32*2*pi);
Xt(:,2)=sin(X(:,2)/32*2*pi);
figure(4); plot(Xt(Y==0,1),Xt(Y==0,2),'b.',Xt(Y>0,1),Xt(Y>0,2),'r.')
Xt(:,1)=sin(X(:,1)/64*2*pi);
Xt(:,2)=sin(X(:,2)/64*2*pi);
figure(4); plot(Xt(Y==0,1),Xt(Y==0,2),'b.',Xt(Y>0,1),Xt(Y>0,2),'r.')
ind=randperm(size(X,1));
k_l=ind(1:10);
RBF=@(X,gamma,x_special)exp(-gamma*(X(:,1)-x_special(1)).^2-gamma*(X(:,2)-x_special(2)).^2);
for k=1:length(k_l)
end
k_l=ind(1:10);
>> RBF=@(X,gamma,x_special)exp(-gamma*(X(:,1)-x_special(1)).^2-gamma*(X(:,2)-x_special(2)).^2);
>> for k=1:length(k_l)
end
k_l=ind(1:10);
RBF=@(X,gamma,x_special)exp(-gamma*(X(:,1)-x_special(1)).^2-gamma*(X(:,2)-x_special(2)).^2);
Xt2=zeros(size(X));
for k=1:length(k_l)
Xt2(:,1)=Xt2(:,1)+RBF(X,X(k_l(k))*(Y(k_l(k))>0);
Xt2(:,2)=Xt2(:,2)+RBF(X,X(k_l(k))*(Y(k_l(k))==0);
end
Xt2(:,1)=Xt2(:,1)+RBF(X,X(k_l(k)).*(Y(k_l(k))>0);
Xt2(:,1)=Xt2(:,1)+RBF(X,X(k_l(k))).*(Y(k_l(k))>0);
max(max(X))
k_l=ind(1:10);
RBF=@(X,gamma,x_special)exp(-gamma*(X(:,1)-x_special(1)).^2-gamma*(X(:,2)-x_special(2)).^2);
Xt2=zeros(size(X));
gamma=1/255^2
for k=1:length(k_l)
Xt2(:,1)=Xt2(:,1)+RBF(X,gamma,X(k_l(k))).*(Y(k_l(k))>0);
Xt2(:,2)=Xt2(:,2)+RBF(X,gamma,X(k_l(k))).*(Y(k_l(k))==0);
end
k_l=ind(1:10);
RBF=@(X,gamma,x_special)exp(-gamma*(X(:,1)-x_special(1)).^2-gamma*(X(:,2)-x_special(2)).^2);
Xt2=zeros(size(X));
gamma=1/255^2
for k=1:length(k_l)
Xt2(:,1)=Xt2(:,1)+RBF(X,gamma,X(k_l(k),:)).*(Y(k_l(k))>0);
Xt2(:,2)=Xt2(:,2)+RBF(X,gamma,X(k_l(k),:)).*(Y(k_l(k))==0);
end
figure(5); plot(Xt2(Y==0,1),Xt2(Y==0,2),'b.',Xt2(Y>0,1),Xt2(Y>0,2),'r.')
k_l=ind(1:10);
RBF=@(X,gamma,x_special)exp(-gamma*(X(:,1)-x_special(1)).^2-gamma*(X(:,2)-x_special(2)).^2);
Xt2=zeros(size(X));
gamma=1/255
for k=1:length(k_l)
Xt2(:,1)=Xt2(:,1)+RBF(X,gamma,X(k_l(k),:)).*(Y(k_l(k))>0);
Xt2(:,2)=Xt2(:,2)+RBF(X,gamma,X(k_l(k),:)).*(Y(k_l(k))==0);
end
figure(5); plot(Xt2(Y==0,1),Xt2(Y==0,2),'b.',Xt2(Y>0,1),Xt2(Y>0,2),'r.')
%%%%%%%%%%%%%%%%%%%%%%%

k_l=ind(1:10); 
RBF=@(X,gamma,x_special)exp(-gamma*(X(:,1)-x_special(1)).^2-gamma*(X(:,2)-x_special(2)).^2);
Xt2=zeros(size(X));
gamma=1/255
for k=1:length(k_l)
  Xt2(:,1)=Xt2(:,1)+RBF(X,gamma,X(k_l(k),:)).*(Y(k_l(k))>0);
  Xt2(:,2)=Xt2(:,2)+RBF(X,gamma,X(k_l(k),:)).*(Y(k_l(k))==0);
end