diff toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/fhme.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/fhme.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,109 @@
+function risultati = fhme(net, nodes_info, data, n)
+%HMEFWD	Forward propagation through an HME model
+%
+% Each row of the (n x class_num) matrix 'risultati' containes the estimated class posterior prob.
+%
+% ----------------------------------------------------------------------------------------------------
+% -> [e-mail]   or   -> [e-mail]
+% ----------------------------------------------------------------------------------------------------
+%
+ns=net.node_sizes;
+if nargin==3
+    ndata=n;
+else
+    ndata=size(data, 1);
+end
+altezza=size(ns,2);
+coeff=cell(altezza-1,1);
+for m=1:ndata
+    %- i=2 --------------------------------------------------------------------------------------
+    s=struct(net.CPD{2});    
+    if nodes_info(1,2)==0,
+        mu=[]; W=[]; predict=[];
+        mu=s.mean(:,:);
+        W=s.weights(:,:,:);
+        predict=mu(:,:)+W(:,:,:)*data(m,:)';            
+        coeff{1,1}=predict';            
+    elseif nodes_info(1,2)==1,
+        coeff{1,1}=fglm(s.glim{1}, data(m,:));
+    else,
+        coeff{1,1}=fmlp(s.mlp{1}, data(m,:));
+    end
+    %----------------------------------------------------------------------------------------------
+    if altezza>3,
+        for i=3:altezza-1,
+            s=[]; f=[]; dpsz=[];
+            f=family(net.dag,i); f=f(2:end-1); dpsz=prod(ns(f));
+            s=struct(net.CPD{i});
+            for j=1:dpsz,
+                if nodes_info(1,i)==1,
+                    coeff{i-1,1}(j,:)=coeff{i-2,1}(1,j)*fglm(s.glim{j}, data(m,:));
+                else
+                    coeff{i-1,1}(j,:)=coeff{i-2,1}(1,j)*fmlp(s.mlp{j}, data(m,:));
+                end
+            end       
+            app=cat(2, coeff{i-1,1}(:)); coeff{i-1,1}=app'; clear app;
+        end
+    end
+    %- i=altezza ----------------------------------------------------------------------------------
+    if altezza>2,
+        i=altezza;
+        s=[]; f=[]; dpsz=[];
+        f=family(net.dag,i); f=f(2:end-1); dpsz=prod(ns(f));
+        s=struct(net.CPD{i});
+        if nodes_info(1,i)==0,            
+            mu=[]; W=[];
+            mu=s.mean(:,:);
+            W=s.weights(:,:,:);
+        end
+        for j=1:dpsz,
+            if nodes_info(1,i)==0,            
+                predict=[];
+                predict=mu(:,j)+W(:,:,j)*data(m,:)';            
+                coeff{i-1,1}(j,:)=coeff{i-2,1}(1,j)*predict';            
+            elseif nodes_info(1,i)==1,
+                coeff{i-1,1}(j,:)=coeff{i-2,1}(1,j)*fglm(s.glim{j}, data(m,:));
+            else
+                coeff{i-1,1}(j,:)=coeff{i-2,1}(1,j)*fmlp(s.mlp{j}, data(m,:));
+            end
+        end
+    end
+    %----------------------------------------------------------------------------------------------
+    risultati(m,:)=sum(coeff{altezza-1,1},1);
+    clear coeff; coeff=cell(altezza-1,1);
+end
+return
+
+%-------------------------------------------------------------------
+
+function [y, a] = fglm(net, x)
+%GLMFWD	Forward propagation through 1-layer net->GLM statistical model
+
+ndata = size(x, 1);
+
+a = x*net.w1 + ones(ndata, 1)*net.b1;
+
+nout = size(a,2);
+% Ensure that sum(exp(a), 2) does not overflow
+maxcut = log(realmax) - log(nout);
+% Ensure that exp(a) > 0
+mincut = log(realmin);
+a = min(a, maxcut);
+a = max(a, mincut);
+temp = exp(a);
+y = temp./(sum(temp, 2)*ones(1,nout));
+
+%-------------------------------------------------------------------
+
+function [y, z, a] = fmlp(net, x)
+%MLPFWD	Forward propagation through 2-layer network.
+
+ndata = size(x, 1);
+
+z = tanh(x*net.w1 + ones(ndata, 1)*net.b1);
+a = z*net.w2 + ones(ndata, 1)*net.b2;  
+temp = exp(a);
+nout = size(a,2);
+y = temp./(sum(temp,2)*ones(1,nout));
+
+%-------------------------------------------------------------------