deterministic_boltzmann.m

数据挖掘matlab源码

function D = Deterministic_Boltzmann(train_features, train_targets, params, region);

% Classify using the deterministic Boltzmann algorithm
% Inputs:
% 	features	- Train features
%	targets	- Train targets
%	Params  - [Ni, Nh, eta, Type, param], where:
%						Ni 	- Number of input units
%						Nh 	- Number of hidden units
%						eta	- Cooling rate
%						Type	- Type of weak learner
%						param - Parameter for the weak learner
%
% Outputs
%	D			- Decision sufrace
%
% In this implementation, we train Ni weak learners, and find the best combination of 
% these classifiers

[Nio,M]   = size(train_features);
No			 = 1;
[Ni, Nh, eta, type, Wparams] = process_params(params);

eta_anneal = 0.95;	%Eta for the anneal process for the examples
iter		 = 0;
max_iter  = 1e5;
DispIter  = 50;
update   = 0;
err		 = 1;
min_err	 = 0.01;

%First, build the weak learners and find the appropriate label for each input pattern 
%for these classifiers
N			 = region(5);
Dw			 = zeros(Ni, N, N);
features	 = zeros(Ni, M);

%First, build the weak learners on a subset of the data
indices	 = randperm(M);
in_mat	 = reshape(indices(1:floor(M/Ni)*Ni),Ni,floor(M/Ni));
for i = 1:Ni,
   Dw(i,:,:) = feval(type, train_features(:,in_mat(i,:)), train_targets(in_mat(i,:)), Wparams, region);
end

%Now, find the targets of the weak classifiers for each input pattern
enum_features = round((train_features - region([1,3])'*ones(1,M))./((region([2,4])-region([1,3]))'*ones(1,M))*region(5));
for i = 1:M,
   features(:,i)	= squeeze(Dw(:,enum_features(2,i), enum_features(1,i)));
end
features = features*2-1;	%Inputs have to be {-1,+1}
targets  = train_targets*2-1;

%BEGIN BOLTZMANN LEARNING
Tini	    = max(eig(cov(features',1)'))/2;    %Initial temperature
Tmin        = 0.01;                             %Stopping temperature
Tb		    = Tini;

%Make a symmetric weight matrix with a zero diagonal
zero_diag   = ones(Ni+Nh+No) - eye(Ni+Nh+No);
W		    = rand(Ni+Nh+No)*2*sqrt(3/(Ni+Nh+No))-sqrt(3/(Ni+Nh+No));
W		    = W.*zero_diag;
for i = 1:Ni+Nh+No,
    for j = i:Ni+Nh+No,
        W(i,j) = W(j,i);
    end
end


while ((iter < max_iter) & (Tb > Tmin)),
   %Randomally select a training pattern
   in	= floor(rand(1)*M)+1;
   x	= features(:,in);
   t  = targets(in);
   
   %Randomize input states Si
   Sin = (rand(Nh,1)>0.5)*2-1;
   
   %Anneal network with input and output clamped
   T = Tini;
   while (T > Tmin),
      %Select a node randomally
      i	= floor(rand(1)*Nh)+1;
      
      %li<-sigma(w_ij*s_j)
      S	= [x; Sin; t]; 
      l	= W(:,i+Ni)'*S;
      
      %Si<-f(li, T(k))
      Sin(i)	= tanh(l/T);
      
      %Lower the temperature
      T = T*eta_anneal;   
   end
   
   %At final, low T, calculate [SiSj]alpha_i,alpha_o clamped
   S	= [x; Sin; t]; 
   SiSj_io_clamped = S*S';
   
   %Randomize input states Si
   Sin= (rand(Nh+No,1)>0.5)*2-1;
   
   %Anneal network with input clamped but output free
   T = Tini;
   while (T > Tmin),
      %Select a node randomally
      i	= floor(rand(1)*(Nh+No))+1;
      
      %li<-sigma(w_ij*s_j)
      S	= [x; Sin]; 
      l	= W(:,i+Ni)'*S;
      
      %Si<-f(li, T(k))
      Sin(i)	= tanh(l/T);
      
      %Lower the temperature
      T = T*eta_anneal;   
   end
   
   %At final, low T, calculate [SiSj]alpha_i clamped
   S	= [x; Sin];
   SiSj_i_clamped = S*S';
   
   %Wij<-Wij + eta/T([SiSj]alpha_i,alpha_o clamped - [SiSj]alpha_i clamped)
   dW = (SiSj_io_clamped - SiSj_i_clamped).*zero_diag;
   W  = W + eta*Tb*dW;
   
   Tb 	= Tb * eta;
   iter	= iter + 1;
   if (floor(iter/DispIter) == iter/DispIter),
      disp(['Iteration ' num2str(iter) ': Temperature is ' num2str(Tb), ' Average weight update is: ' num2str(update/DispIter)])
      update = 0;
  else
      update = update + sum(sum(abs(dW)))/size(dW,1)^2;
   end
   
end

%Build the decision region
disp('Building decision region')
flatxy = zeros(Ni, N^2);
for i = 1:Ni,
   flat			= squeeze(Dw(i,:,:));
   flatxy(i,:) = flat(:)';
end
flatxy = flatxy*2-1;

%Randomize input states Si
Sin= (rand(Nh+No,N^2)>0.5)*2-1;

%Anneal network with input clamped but output free
T = Tini;
while (T > Tmin),
   %Select a node randomally
   i	= floor(rand(1)*(Nh+No))+1;
   
   %li<-sigma(w_ij*s_j)
   S	= [flatxy; Sin]; 
   l	= W(:,i+Ni)'*S;
   
   %Si<-f(li, T(k))
   Sin(i,:)	= tanh(l/T);
   
   %Lower the temperature
   T = T*eta_anneal;   
end

So = Sin(Nh+1:end,:);
D	= reshape(So>0,N,N);