Merge pull request #1 from ziejd2/octave_php_separate

Octave php separate

16 files changed, 963 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/CVS/Entries b/sourcecodes/bnt-master/BNT/examples/static/HME/CVS/Entries
new file mode 100644
index 00000000..b27a9df8
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/HME/CVS/Entries
@@ -0,0 +1,14 @@
+/HMEforMatlab.jpg/1.1.1.1/Wed May 29 15:59:54 2002//
+/README/1.1.1.1/Wed May 29 15:59:54 2002//
+/fhme.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/gen_data.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/hme_class_plot.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/hme_reg_plot.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/hme_topobuilder.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/hmemenu.m/1.1.1.1/Thu Feb 12 12:57:28 2004//
+/test_data_class.mat/1.1.1.1/Wed May 29 15:59:54 2002//
+/test_data_class2.mat/1.1.1.1/Wed May 29 15:59:54 2002//
+/test_data_reg.mat/1.1.1.1/Wed May 29 15:59:54 2002//
+/train_data_class.mat/1.1.1.1/Wed May 29 15:59:54 2002//
+/train_data_reg.mat/1.1.1.1/Wed May 29 15:59:54 2002//
+D
diff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/CVS/Repository b/sourcecodes/bnt-master/BNT/examples/static/HME/CVS/Repository
new file mode 100644
index 00000000..2ac6a351
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/HME/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/examples/static/HME
diff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/CVS/Root b/sourcecodes/bnt-master/BNT/examples/static/HME/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/HME/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/HMEforMatlab.jpg b/sourcecodes/bnt-master/BNT/examples/static/HME/HMEforMatlab.jpg
new file mode 100644
index 00000000..16682678
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/HME/HMEforMatlab.jpg
Binary files differdiff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/README b/sourcecodes/bnt-master/BNT/examples/static/HME/README
new file mode 100644
index 00000000..4c794975
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/HME/README
@@ -0,0 +1,2 @@
+This directory contains code for hierarchical mixture of experts,
+written by Pierpaolo Brutti (May 2001). Run the file hmemenu to get started.
diff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/fhme.m b/sourcecodes/bnt-master/BNT/examples/static/HME/fhme.m
new file mode 100644
index 00000000..6aeb7196
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/HME/fhme.m
@@ -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.
+%
+% ----------------------------------------------------------------------------------------------------
+% -> pierpaolo_b@hotmail.com   or   -> pampo@interfree.it
+% ----------------------------------------------------------------------------------------------------
+%
+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));
+
+%-------------------------------------------------------------------
diff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/gen_data.m b/sourcecodes/bnt-master/BNT/examples/static/HME/gen_data.m
new file mode 100644
index 00000000..c37f9d28
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/HME/gen_data.m
@@ -0,0 +1,52 @@
+function [data, ndata1, ndata2, targets]=gen_data(ndata, seed)
+% Generate data from three classes in 2d
+% Setting 'seed' for reproducible results
+% OUTPUT
+% data : data set
+% ndata1, ndata2: separator
+
+if nargin<1,
+   error('Missing data size');
+end
+
+input_dim = 2;
+num_classes = 3;
+
+if nargin==2,
+   % Fix seeds for reproducible results
+   randn('state', seed);
+   rand('state', seed);
+end
+
+% Generate mixture of three Gaussians in two dimensional space
+data = randn(ndata, input_dim);
+targets = zeros(ndata, 3);
+
+% Priors for the clusters
+prior(1) = 0.4;
+prior(2) = 0.3;
+prior(3) = 0.3;
+
+% Cluster centres
+c = [2.0, 2.0; 0.0, 0.0; 1, -1];
+
+ndata1 = round(prior(1)*ndata);
+ndata2 = round((prior(1) + prior(2))*ndata);
+% Put first cluster at (2, 2)
+data(1:ndata1, 1) = data(1:ndata1, 1) * 0.5 + c(1,1);
+data(1:ndata1, 2) = data(1:ndata1, 2) * 0.5 + c(1,2);
+targets(1:ndata1, 1) = 1;
+
+% Leave second cluster at (0,0)
+data((ndata1 + 1):ndata2, :) = data((ndata1 + 1):ndata2, :);
+targets((ndata1+1):ndata2, 2) = 1;
+
+data((ndata2+1):ndata, 1) = data((ndata2+1):ndata,1) *0.6 + c(3, 1);
+data((ndata2+1):ndata, 2) = data((ndata2+1):ndata,2) *0.6 + c(3, 2);
+targets((ndata2+1):ndata, 3) = 1;
+
+if 0
+  ndata = 1;
+  data = x;
+  targets = [1 0 0];
+end
diff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/hme_class_plot.m b/sourcecodes/bnt-master/BNT/examples/static/HME/hme_class_plot.m
new file mode 100644
index 00000000..de60c2ee
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/HME/hme_class_plot.m
@@ -0,0 +1,142 @@
+function fh=hme_class_plot(net, nodes_info, train_data, test_data)
+% 
+% Use this function ONLY when the input dimension is 2
+% and the problem is a classification one.
+% We assume that each row of 'train_data' & 'test_data' is an example.
+%
+%------Line Spec------------------------------------------------------------------------
+%
+% LineWidth       - specifies the width (in points) of the line
+% MarkerEdgeColor - specifies the color of the marker or the edge color
+%                   forfilled markers (circle, square, diamond, pentagram, hexagram, and the
+%                   four triangles).
+% MarkerFaceColor - specifies the color of the face of filled markers.
+% MarkerSize      - specifies the size of the marker in points.
+%
+% Example
+% -------
+% plot(t,sin(2*t),'-mo',...
+%                'LineWidth',2,...
+%                'MarkerEdgeColor','k',...                          % 'k'=black
+%                'MarkerFaceColor',[.49 1 .63],...                  % RGB color
+%                'MarkerSize',12)
+%----------------------------------------------------------------------------------------
+
+class_num=nodes_info(2,end);
+mn_x = round(min(train_data(:,1)));     mx_x = round(max(train_data(:,1)));
+mn_y = round(min(train_data(:,2)));     mx_y = round(max(train_data(:,2)));
+if nargin==4,
+    mn_x = round(min([train_data(:,1); test_data(:,1)]));     
+    mx_x = round(max([train_data(:,1); test_data(:,1)]));
+    mn_y = round(min([train_data(:,2); test_data(:,2)]));
+    mx_y = round(max([train_data(:,1); test_data(:,2)]));
+end
+x = mn_x(1)-1:0.2:mx_x(1)+1;
+y = mn_y(1)-1:0.2:mx_y(1)+1;
+[X, Y] = meshgrid(x,y);
+X = X(:); 
+Y = Y(:);
+num_g=size(X,1);
+griglia = [X Y];
+rand('state',1);
+if class_num<=6,
+    colors=['r'; 'g'; 'b'; 'c'; 'm'; 'y'];
+else
+    colors=rand(class_num, 3);  % each row is an RGB color
+end
+fh = figure('Name','Data & decision boundaries', 'MenuBar', 'none', 'NumberTitle', 'off');
+ms=5;           % Marker Size
+if nargin==4,
+%    ms=4;       % Marker Size
+    subplot(1,2,1);
+end
+% Plot of train_set -------------------------------------------------------------------------
+axis([mn_x-1 mx_x+1 mn_y-1 mx_y+1]);
+set(gca, 'Box', 'on');
+c_max_train = max(train_data(:,3));
+hold on
+for m=1:c_max_train,
+    app_x=train_data(:,1);
+    app_y=train_data(:,2);
+    thisX=app_x(train_data(:,3)==m);
+    thisY=app_y(train_data(:,3)==m);
+    if class_num<=6,
+       str_col=[];
+       str_col=['o', colors(m,:)];
+       plot(thisX, thisY, str_col, 'MarkerSize', ms);
+    else
+        plot(thisX, thisY, 'o',...
+            'LineWidth', 1,...            
+            'MarkerEdgeColor', colors(m,:), 'MarkerSize', ms)
+    end
+end
+%---hmefwd_generale(net,data,ndata)-----------------------------------------------------------
+Z=fhme(net, nodes_info, griglia, num_g);      % forward propagation trougth the HME
+%---------------------------------------------------------------------------------------------
+[foo , class] = max(Z'); % 0/1 loss function => we assume that the true class is the one with the
+                         % maximum posterior prob.
+class = class';
+for m = 1:class_num,
+  thisX=[]; thisY=[];
+  thisX = X(class == m);
+  thisY = Y(class == m);
+  if class_num<=6,
+      str_col=[];
+      str_col=['d', colors(m,:)];
+      h=plot(thisX, thisY, str_col);      
+  else
+      h = plot(thisX, thisY, 'd',...
+          'MarkerEdgeColor',colors(m,:),...
+          'MarkerFaceColor','w');
+  end
+  set(h, 'MarkerSize', 4);
+end
+title('Training set and Decision Boundaries (0/1 loss)')
+hold off
+
+% Plot of test_set --------------------------------------------------------------------------
+if nargin==4,
+    subplot(1,2,2);
+    axis([mn_x-1 mx_x+1 mn_y-1 mx_y+1]);
+    set(gca, 'Box', 'on');
+    hold on     
+    if size(test_data,2)==3,  % we know the classification of the test set examples
+        c_max_test = max(test_data(:,3));
+        for m=1:c_max_test,
+            app_x=test_data(:,1);
+            app_y=test_data(:,2);
+            thisX=app_x(test_data(:,3)==m);
+            thisY=app_y(test_data(:,3)==m);
+            if class_num<=6,
+                str_col=[];
+                str_col=['o', colors(m,:)];
+                plot(thisX, thisY, str_col, 'MarkerSize', ms);
+            else
+                plot(thisX, thisY, 'o',...
+                     'LineWidth', 1,...
+                     'MarkerEdgeColor', colors(m,:),...
+                     'MarkerSize',ms);
+            end
+        end
+    else
+        plot(test_data(:,1), test_data(:,2), 'ko',...
+            'MarkerSize', ms);
+    end
+    for m = 1:class_num,
+        thisX=[]; thisY=[];
+        thisX = X(class == m);
+        thisY = Y(class == m);
+        if class_num<=6,
+          str_col=[];
+          str_col=['d', colors(m,:)];
+          h=plot(thisX, thisY, str_col);  
+        else
+           h = plot(thisX, thisY, 'd',...
+                    'MarkerEdgeColor', colors(m,:),...
+                    'MarkerFaceColor','w');
+        end
+        set(h, 'MarkerSize', 4);
+    end
+    title('Test set and Decision Boundaries (0/1 loss)')
+    hold off
+end
\ No newline at end of file
diff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/hme_reg_plot.m b/sourcecodes/bnt-master/BNT/examples/static/HME/hme_reg_plot.m
new file mode 100644
index 00000000..510e96c2
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/HME/hme_reg_plot.m
@@ -0,0 +1,43 @@
+function fh=hme_reg_plot(net, nodes_info, train_data, test_data)
+% 
+% Use this function ONLY when the input dimension is 1
+% and the problem is a regression one.
+% We assume that each row of 'train_data' & 'test_data' is an example.
+%
+% ----------------------------------------------------------------------------------------------------
+% -> pierpaolo_b@hotmail.com   or   -> pampo@interfree.it
+% ----------------------------------------------------------------------------------------------------
+
+fh=figure('Name','HME based regression', 'MenuBar', 'none', 'NumberTitle', 'off');
+
+mn_x_train = round(min(train_data(:,1)));
+mx_x_train = round(max(train_data(:,1)));     
+x_train = mn_x_train(1):0.01:mx_x_train(1);
+Z_train=fhme(net, nodes_info, x_train',size(x_train,2));      % forward propagation trougth the HME
+
+if nargin==4,
+    subplot(2,1,1);
+    mn_x_test = round(min(test_data(:,1)));
+    mx_x_test = round(max(test_data(:,1)));
+    x_test = mn_x_test(1):0.01:mx_x_test(1);
+    Z_test=fhme(net, nodes_info, x_test',size(x_test,2));      % forward propagation trougth the HME
+end
+
+hold on;
+set(gca, 'Box', 'on');
+plot(x_train', Z_train, 'r');
+plot(train_data(:,1),train_data(:,2),'+k');
+title('Training set and prediction');
+hold off
+
+if nargin==4,
+    subplot(2,1,2);
+    hold on;
+    set(gca, 'Box', 'on');
+    plot(x_train', Z_train, 'r');
+    if size(test_data,2)==2,
+        plot(test_data(:,1),test_data(:,2),'+k');
+    end
+    title('Test set and prediction');
+    hold off
+end
\ No newline at end of file
diff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/hme_topobuilder.m b/sourcecodes/bnt-master/BNT/examples/static/HME/hme_topobuilder.m
new file mode 100644
index 00000000..0893bc38
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/HME/hme_topobuilder.m
@@ -0,0 +1,47 @@
+function [bnet, onodes]=hme_topobuilder(nodes_info);
+%
+% HME topology builder
+%
+% ----------------------------------------------------------------------------------------------------
+% -> pierpaolo_b@hotmail.com   or   -> pampo@interfree.it
+% ----------------------------------------------------------------------------------------------------
+
+nodes_num=size(nodes_info,2);
+dag = zeros(nodes_num);
+list=[1:nodes_num];
+for i=1:(nodes_num-1)
+    app=[];
+    app=list((i+1):end);
+    dag(i,app) = 1;
+end
+onodes = [1 nodes_num];                           
+dnodes = list(2:end-1);
+if nodes_info(1,end)>0,
+    dnodes=[dnodes nodes_num];
+end
+ns = nodes_info(2,:);
+
+bnet = mk_bnet(dag, ns, dnodes);
+clamped = 0;
+
+bnet.CPD{1} = root_CPD(bnet, 1);
+
+rand('state', 50);
+randn('state', 50);
+
+for i=2:nodes_num,
+    if (nodes_info(1,i)==0)&(nodes_info(4,i)==1),
+        bnet.CPD{i} = gaussian_CPD(bnet, i, [], [], [], 'full');
+    elseif (nodes_info(1,i)==0)&(nodes_info(4,i)==2),
+        bnet.CPD{i} = gaussian_CPD(bnet, i, [], [], [], 'diag');
+    elseif (nodes_info(1,i)==0)&(nodes_info(4,i)==3),
+        bnet.CPD{i} = gaussian_CPD(bnet, i, [], [], [], 'full', 'tied');
+    elseif (nodes_info(1,i)==0)&(nodes_info(4,i)==4),
+        bnet.CPD{i} = gaussian_CPD(bnet, i, [], [], [], 'diag', 'tied');        
+    elseif nodes_info(1,i)==1,
+        %bnet.CPD{i} = dsoftmax_CPD(bnet, i, [], [], clamped, nodes_info(4,i));
+	bnet.CPD{i} = softmax_CPD(bnet, i, 'clamped', clamped, 'max_iter', nodes_info(4,i));
+    else
+        bnet.CPD{i} = mlp_CPD(bnet, i, nodes_info(3,i), [], [], [], [], clamped, nodes_info(4,i));
+    end
+end
diff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/hmemenu.m b/sourcecodes/bnt-master/BNT/examples/static/HME/hmemenu.m
new file mode 100644
index 00000000..64762b8e
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/HME/hmemenu.m
@@ -0,0 +1,552 @@
+% dataset      -> (1=>user data) or (2=>toy example)
+% type         -> (1=> Regression model) or (2=>Classification model)
+% num_glevel   -> number of hidden nodes in the net (gating levels)
+% num_exp      -> number of experts in the net
+% branch_fact  -> dimension of the hidden nodes in the net
+% cov_dim      -> root node dimension
+% res_dim      -> output node dimension
+% nodes_info   -> 4 x num_glevel+2 matrix that contain all the info about the nodes:
+%                 nodes_info(1,:) = nodes type: (0=>gaussian)or(1=>softmax)or(2=>mlp)
+%                 nodes_info(2,:) = nodes size: [cov_dim   num_glevel x branch_fact   res_dim]
+%                 nodes_info(3,:) = hidden units number (for mlp nodes)
+%                                  |- optimizer iteration number (for softmax & mlp CPD)
+%                 nodes_info(4,:) =|- covariance type (for gaussian CPD)-> 
+%                                  | (1=>Full)or(2=>Diagonal)or(3=>Full&Tied)or(4=>Diagonal&Tied)
+% fh1 -> Figure: data & decizion boundaries; fh2 -> confusion matrix; fh3 -> LL trace                                                                       
+% test_data    -> test data matrix
+% train_data   -> training data matrix
+% ntrain       -> size(train_data,2)
+% ntest        -> size(test_data,2)
+% cases        -> (cell array) training data formatted for the learning engine
+% bnet         -> bayesian net before learning
+% bnet2        -> bayesian net after learning
+% ll           -> log-likelihood before learning
+% LL2          -> log-likelihood trace
+% onodes       -> obs nodes in bnet & bnet2
+% max_em_iter  -> maximum number of interations of the EM algorithm
+% train_result -> prediction on the training set (as test_result)
+% 
+% IMPORTANT: CHECK the loading path (lines 64 & 364)
+% ----------------------------------------------------------------------------------------------------
+% -> pierpaolo_b@hotmail.com   or   -> pampo@interfree.it
+% ----------------------------------------------------------------------------------------------------
+
+error('this no longer works with the latest version of BNT')
+
+clear all;
+clc;
+disp('---------------------------------------------------');
+disp('  Hierarchical Mixtures of Experts models builder ');
+disp('---------------------------------------------------');
+disp(' ')
+disp('   Using this script you can build both an HME model')
+disp('as in [Wat94] and [Jor94] i.e. with ''softmax'' gating')
+disp('nodes and ''gaussian'' ( for regression ) or ''softmax''') 
+disp('( for classification ) expert node, and its variants')
+disp('called ''gated nets'' where we use ''mlp'' models in')
+disp('place of a number of ''softmax'' ones [Mor98], [Wei95].')
+disp('  You can decide to train and test the model on your')
+disp('datasets  or  to evaluate its  performance on  a toy')
+disp('example.')
+disp(' ')
+disp('Reference')
+disp('[Mor98] P. Moerland (1998):')
+disp('        Localized mixtures of experts. (http://www.idiap.ch/~perry/)')
+disp('[Jor94] M.I. Jordan, R.A. Jacobs (1994):')
+disp('        HME and the EM algorithm. (http://www.cs.berkeley.edu/~jordan/)')
+disp('[Wat94] S.R. Waterhouse, A.J. Robinson (1994):') 
+disp('        Classification using HME. (http://www.oigeeza.com/steve/)')
+disp('[Wei95] A.S. Weigend, M. Mangeas (1995):') 
+disp('        Nonlinear gated experts for time series.')
+disp(' ')
+
+if 0
+disp('(See the figure)')
+pause(5);
+%%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+im_path=which('HMEforMatlab.jpg');
+fig=imread(im_path, 'jpg');
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+figure('Units','pixels','MenuBar','none','NumberTitle','off', 'Name', 'HME model');
+image(fig); 
+axis image;
+axis off;
+clear fig;
+set(gca,'Position',[0 0 1 1])
+disp('(Press any key to continue)')
+pause
+end
+
+clc
+disp('---------------------------------------------------');
+disp('              Specify the Architecture             ');
+disp('---------------------------------------------------');
+disp(' ');
+disp('What kind of model do you need?')
+disp(' ')
+disp('1) Regression ')
+disp('2) Classification')
+disp(' ')
+type=input('1 or 2?: ');
+if (isempty(type)|(~ismember(type,[1 2]))), error('Invalid value'); end
+clc
+disp('----------------------------------------------------');
+disp('               Specify the Architecture             ');
+disp('----------------------------------------------------');
+disp(' ')
+disp('Now you have to set the number of experts and gating')
+disp('levels in the net.  This script builds only balanced')
+disp('hierarchy with the same branching factor (>1)at each')
+disp('(gating) level. So remember that: ')
+disp(' ')
+disp('         num_exp = branch_fact^num_glevel           ')
+disp(' ')
+disp('with branch_fact >=2.')
+disp('You can also set to zeros the number of gating level')
+disp('in order to obtain a classical GLM model.           ')
+disp(' ')
+disp('----------------------------------------------------');
+disp(' ')
+num_glevel=input('Insert the number of gating levels {0,...,20}: ');
+if (isempty(num_glevel)|(~ismember(num_glevel,[0:20]))), error('Invalid value'); end
+nodes_info=zeros(4,num_glevel+2);
+if num_glevel>0, %------------------------------------------------------------------------------------
+    for i=2:num_glevel+1,
+        clc
+        disp('----------------------------------------------------');
+        disp('               Specify the Architecture             ');
+        disp('----------------------------------------------------');
+        disp(' ')   
+        disp(['-> Gating network ', num2str(i-1), ' is a: '])
+        disp(' ')
+        disp('   1) Softmax model');
+        disp('   2) Two layer perceptron model')
+        disp(' ')
+        nodes_info(1,i)=input('1 or 2?: ');
+        if (isempty(nodes_info(1,i))|(~ismember(nodes_info(1,i),[1 2]))), error('Invalid value'); end
+        disp(' ')
+        if nodes_info(1,i)==2,
+           nodes_info(3,i)=input('Insert the number of units in the hidden layer: ');
+           if (isempty(nodes_info(3,i))|(floor(nodes_info(3,i))~=nodes_info(3,i))|(nodes_info(3,i)<=0)), 
+              error(['Invalid value: ', num2str(nodes_info(3,i)), ' is not a positive integer!']);
+           end
+           disp(' ')
+        end
+        nodes_info(4,i)=input('Insert the optimizer iteration number: ');
+        if (isempty(nodes_info(4,i))|(floor(nodes_info(4,i))~=nodes_info(4,i))|(nodes_info(4,i)<=0)), 
+           error(['Invalid value: ', num2str(nodes_info(4,i)), ' is not a positive integer!']);
+        end    
+    end
+    clc
+    disp('---------------------------------------------------------');
+    disp('                 Specify the Architecture                ');
+    disp('---------------------------------------------------------');
+    disp(' ')
+    disp('Now you have to set the number  of experts in the network');
+    disp('The value will be adjusted in order to obtain a hierarchy');
+    disp('as said above.')
+    disp(' ');    
+    num_exp=input(['Insert the approximative number of experts (>=', num2str(2^num_glevel), '): ']);
+    if (isempty(num_exp)|(num_exp<=0)|(num_exp<2^num_glevel)), 
+        error('Invalid value');
+    end
+    app1=0; base=2;
+    while app1<num_exp,
+        app1=base^num_glevel;
+        base=base+1;
+    end
+    app2=(base-2)^num_glevel;
+    branch_fact=base-1;
+    if app2>=(2^num_glevel)&(abs(app2-num_exp)<abs(app1-num_exp)),
+        branch_fact=base-2;
+    end
+    clear app1 app2 base;
+    disp(' ')
+    disp(['The effective number of experts in the net is: ', num2str(branch_fact^num_glevel), '.'])
+    disp(' ');
+else
+    clc
+    disp('---------------------------------------------------------');
+    disp('        Specify the Architecture (GLM model)             ');
+    disp('---------------------------------------------------------');
+    disp(' ')
+end % END of: if num_glevel>0-------------------------------------------------------------------------
+
+if type==2,
+    disp(['-> Expert node is a: '])
+    disp(' ')
+    disp('   1) Softmax model');
+    disp('   2) Two layer perceptron model')
+    disp(' ')
+    nodes_info(1,end)=input('1 or 2?: ');
+    if (isempty(nodes_info(1,end))|(~ismember(nodes_info(1,end),[1 2]))), 
+        error('Invalid value'); 
+    end
+    disp(' ')
+    if nodes_info(1,end)==2,
+       nodes_info(3,end)=input('Insert the number of units in the hidden layer: ');
+       if (isempty(nodes_info(3,end))|(floor(nodes_info(3,end))~=nodes_info(3,end))|(nodes_info(3,end)<=0)), 
+           error(['Invalid value: ', num2str(nodes_info(3,end)), ' is not a positive integer!']);
+       end
+       disp(' ')
+    end
+    nodes_info(4,end)=input('Insert the optimizer iteration number: ');
+    if (isempty(nodes_info(4,end))|(floor(nodes_info(4,end))~=nodes_info(4,end))|(nodes_info(4,end)<=0)), 
+        error(['Invalid value: ', num2str(nodes_info(4,end)), ' is not a positive integer!']);
+    end
+elseif type==1,
+    disp('What kind of covariance matrix structure do you want?')
+    disp(' ')
+    disp('   1) Full');
+    disp('   2) Diagonal')
+    disp('   3) Full & Tied');
+    disp('   4) Diagonal & Tied')
+
+    disp(' ')
+    nodes_info(4,end)=input('1, 2, 3 or 4?: ');
+    if (isempty(nodes_info(4,end))|(~ismember(nodes_info(4,end),[1 2 3 4]))), 
+        error('Invalid value'); 
+    end  
+end
+clc
+disp('----------------------------------------------------');
+disp('                    Specify the Input               ');
+disp('----------------------------------------------------');
+disp(' ')
+disp('Do you want to...')
+disp(' ')
+disp('1) ...use your own dataset?')
+disp('2) ...apply the model on a toy example?')
+disp(' ')
+dataset=input('1 or 2?: ');
+if (isempty(dataset)|(~ismember(dataset,[1 2]))), error('Invalid value'); end
+if dataset==1,
+    if type==1,
+        clc
+        disp('-------------------------------------------------------');
+        disp('        Specify the Input - Regression problem         ');
+        disp('-------------------------------------------------------');
+        disp(' ')
+        disp('Be sure that each row of your data matrix is an example');
+        disp('with the covariate values that precede the respond ones')
+        disp(' ')
+        disp('-------------------------------------------------------');
+        disp(' ')
+        cov_dim=input('Insert the covariate space dimension: ');
+        if (isempty(cov_dim)|(floor(cov_dim)~=cov_dim)|(cov_dim<=0)), 
+          error(['Invalid value: ', num2str(cov_dim), ' is not a positive integer!']);
+        end
+        disp(' ')
+        res_dim=input('Insert the dimension of the respond variable: ');
+        if (isempty(res_dim)|(floor(res_dim)~=res_dim)|(res_dim<=0)), 
+            error(['Invalid value: ', num2str(res_dim), ' is not a positive integer!']);
+        end 
+        disp(' ');
+    elseif type==2
+        clc
+        disp('-------------------------------------------------------');
+        disp('      Specify the Input - Classification problem       ');
+        disp('-------------------------------------------------------');
+        disp(' ')
+        disp('Be sure that each row of your data matrix is an example');
+        disp('with the covariate values that precede the class labels');
+        disp('(integer value >=1).                                   ');
+        disp(' ')
+        disp('-------------------------------------------------------');
+        disp(' ')
+        cov_dim=input('Insert the covariate space dimension: ');
+        if (isempty(cov_dim)|(floor(cov_dim)~=cov_dim)|(cov_dim<=0)), 
+          error(['Invalid value: ', num2str(cov_dim), ' is not a positive integer!']);
+        end
+        disp(' ')
+        res_dim=input('Insert the number of classes: ');
+        if (isempty(res_dim)|(floor(res_dim)~=res_dim)|(res_dim<=0)), 
+          error(['Invalid value: ', num2str(res_dim), ' is not a positive integer!']);
+        end        
+        disp(' ')               
+    end    
+    % ------------------------------------------------------------------------------------------------
+    % Loading training data --------------------------------------------------------------------------
+    % ------------------------------------------------------------------------------------------------
+    train_path=input('Insert the complete (with extension) path of the training data file:\n >> ','s');    
+    if isempty(train_path), error('You must specify a data set for training!'); end
+    if ~isempty(findstr('.mat',train_path)),
+        ap=load(train_path); app=fieldnames(ap); train_data=eval(['ap.', app{1,1}]);
+        clear ap app;
+    elseif ~isempty(findstr('.txt',train_path)),
+        train_data=load(train_path, '-ascii');
+    else
+        error('Invalid data format: not a .mat or a .txt file')
+    end
+    if (size(train_data,2)~=cov_dim+res_dim)&(type==1),
+        error(['Invalid data matrix size: ', num2str(size(train_data,2)), ' columns rather than ',...
+            num2str(cov_dim+res_dim),'!']);
+    elseif (size(train_data,2)~=cov_dim+1)&(type==2),
+        error(['Invalid data matrix size: ', num2str(size(train_data,2)), ' columns rather than ',...
+            num2str(cov_dim+1),'!']);    
+    elseif (~isempty(find(ismember(intersect([train_data(:,end)' 1:res_dim],...
+            train_data(:,end)'),[1:res_dim])==0)))&(type==2),
+        error('Invalid class label');
+    end    
+    ntrain=size(train_data,1);
+    train_d=train_data(:,1:cov_dim);
+    if type==2,
+        train_t=zeros(ntrain, res_dim);
+        for m=1:res_dim,
+            train_t((find(train_data(:,end)==m))',m)=1;
+        end
+    else
+        train_t=train_data(:,cov_dim+1:end);
+    end        
+    disp(' ')
+    % ------------------------------------------------------------------------------------------------
+    % Loading test data ------------------------------------------------------------------------------
+    % ------------------------------------------------------------------------------------------------
+    disp('(If you don''t want to specify a test-set press ''return'' only)');
+    test_path=input('Insert the complete (with extension) path of the test data file:\n >> ','s');  
+    if ~isempty(test_path),
+        if ~isempty(findstr('.mat',test_path)),        
+            ap=load(test_path); app=fieldnames(ap); test_data=eval(['ap.', app{1,1}]);
+            clear ap app;
+        elseif ~isempty(findstr('.txt',test_path)),
+            test_data=load(test_path, '-ascii');
+        else
+            error('Invalid data format: not a .mat or a .txt file')
+        end
+        if (size(test_data,2)~=cov_dim)&(size(test_data,2)~=cov_dim+res_dim)&(type==1),
+            error(['Invalid data matrix size: ', num2str(size(test_data,2)), ' columns rather than ',...
+                num2str(cov_dim+res_dim), ' or ', num2str(cov_dim), '!']);
+        elseif (size(test_data,2)~=cov_dim)&(size(test_data,2)~=cov_dim+1)&(type==2),
+            error(['Invalid data matrix size: ', num2str(size(test_data,2)), ' columns rather than ',...
+                num2str(cov_dim+1), ' or ', num2str(cov_dim), '!']);
+        elseif (~isempty(find(ismember(intersect([test_data(:,end)' 1:res_dim],...
+                test_data(:,end)'),[1:res_dim])==0)))&(type==2)&(size(test_data,2)==cov_dim+1),
+            error('Invalid class label');
+        end
+        ntest=size(test_data,1);        
+        test_d=test_data(:,1:cov_dim);
+        if (type==2)&(size(test_data,2)>cov_dim),
+            test_t=zeros(ntest, res_dim);
+            for m=1:res_dim,
+                test_t((find(test_data(:,end)==m))',m)=1;
+            end
+        elseif (type==1)&(size(test_data,2)>cov_dim),
+            test_t=test_data(:,cov_dim+1:end);
+        end
+        disp(' ');
+    end
+else    
+    clc
+    disp('----------------------------------------------------');
+    disp('                  Specify the Input                 ');
+    disp('----------------------------------------------------');
+    disp(' ')
+    ntrain = input('Insert the number of examples in training (<500): ');
+    if (isempty(ntrain)|(floor(ntrain)~=ntrain)|(ntrain<=0)|(ntrain>500)), 
+          error(['Invalid value: ', num2str(ntrain), ' is not a positive integer <500!']);
+    end        
+    disp(' ')
+    test_path='toy';
+    ntest = input('Insert the number of examples in test (<500): ');
+    if (isempty(ntest)|(floor(ntest)~=ntest)|(ntest<=0)|(ntest>500)), 
+          error(['Invalid value: ', num2str(ntest), ' is not a positive integer <500!']);
+    end        
+
+    if type==2,
+        cov_dim=2;
+        res_dim=3;
+        seed = 42;
+        [train_d, ntrain1, ntrain2, train_t]=gen_data(ntrain, seed);
+        for m=1:ntrain
+            q=[]; q = find(train_t(m,:)==1);
+            train_data(m,:)=[train_d(m,:) q];
+        end
+        [test_d, ntest1, ntest2, test_t]=gen_data(ntest);
+        for m=1:ntest
+            q=[]; q = find(test_t(m,:)==1);
+            test_data(m,:)=[test_d(m,:) q];
+        end
+    else
+        cov_dim=1;
+        res_dim=1;
+        global HOME
+        %%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+        load([HOME '/examples/static/Misc/mixexp_data.txt'], '-ascii');
+        %%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+        train_data = mixexp_data(1:ntrain, :);
+        train_d=train_data(:,1:cov_dim); train_t=train_data(:,cov_dim+1:end);
+        test_data = mixexp_data(ntrain+1:ntrain+ntest, :);
+        test_d=test_data(:,1:cov_dim); 
+        if size(test_data,2)>cov_dim,
+            test_t=test_data(:,cov_dim+1:end);
+        end
+    end    
+end
+% Set the nodes dimension-----------------------------------
+if num_glevel>0,
+    nodes_info(2,2:num_glevel+1)=branch_fact;
+end
+nodes_info(2,1)=cov_dim; nodes_info(2,end)=res_dim;
+%-----------------------------------------------------------
+% Prepare the training data for the learning engine---------
+%-----------------------------------------------------------
+cases = cell(size(nodes_info,2), ntrain);
+for m=1:ntrain,
+    cases{1,m}=train_data(m,1:cov_dim)';
+    cases{end,m}=train_data(m,cov_dim+1:end)';
+end
+%-----------------------------------------------------------------------------------------------------
+[bnet onodes]=hme_topobuilder(nodes_info);
+engine = jtree_inf_engine(bnet, onodes);
+clc
+disp('---------------------------------------------------------------------');
+disp('                         L  E  A  R  N  I  N  G                      ');
+disp('---------------------------------------------------------------------');
+disp(' ')
+ll = 0;
+for l=1:ntrain
+  scritta=['example number: ', int2str(l),'---------------------------------------------'];
+  disp(scritta);
+  ev = cases(:,l);
+  [engine, loglik] = enter_evidence(engine, ev);
+  ll = ll + loglik;
+end
+disp(' ')
+disp(['Log-likelihood before learning: ', num2str(ll)]);
+disp(' ')
+disp('(Press any key to continue)');
+pause
+%-----------------------------------------------------------
+clc
+disp('---------------------------------------------------------------------');
+disp('                         L  E  A  R  N  I  N  G                      ');
+disp('---------------------------------------------------------------------');
+disp(' ')
+max_em_iter=input('Insert the maximum number of the EM algorithm iterations: ');
+if (isempty(max_em_iter)|(floor(max_em_iter)~=max_em_iter)|(max_em_iter<=1)), 
+          error(['Invalid value: ', num2str(ntest), ' is not a positive integer >1!']);
+end 
+disp(' ')
+disp(['Log-likelihood before learning: ', num2str(ll)]);
+disp(' ')
+
+[bnet2, LL2] = learn_params_em(engine, cases, max_em_iter);
+disp(' ')
+fprintf('HME: loglik before learning %f, after %d iters %f\n', ll, length(LL2),  LL2(end));
+disp(' ')
+disp('(Press any key to continue)');
+pause
+%-----------------------------------------------------------------------------------
+% Classification problem: plot data & decision boundaries if the input data size = 2
+% Regression problem: plot data & prediction if the input data size = 1
+%-----------------------------------------------------------------------------------
+if (type==2)&(nodes_info(2,1)==2)&(~isempty(test_path)),
+    fh1=hme_class_plot(bnet2, nodes_info, train_data, test_data);
+    disp(' ');
+    disp('(See the figure)');
+elseif (type==2)&(nodes_info(2,1)==2)&(isempty(test_path)),
+    fh1=hme_class_plot(bnet2, nodes_info, train_data);
+    disp(' ');
+    disp('(See the figure)');
+elseif (type==1)&(nodes_info(2,1)==1)&(~isempty(test_path)),
+    fh1=hme_reg_plot(bnet2, nodes_info, train_data, test_data);
+    disp(' ');
+    disp('(See the figure)');
+elseif (type==1)&(nodes_info(2,1)==1)&(isempty(test_path)),
+    fh1=hme_reg_plot(bnet2, nodes_info, train_data);
+    disp(' ')
+    disp('(See the figure)');
+end
+%-----------------------------------------------------------------------------------
+% Classification problem: plot confusion matrix
+%-----------------------------------------------------------------------------------
+if (type==2)
+    ztrain=fhme(bnet2, nodes_info, train_d, size(train_d,1));  
+    [Htrain, trainRate]=confmat(ztrain, train_t); % CM on the training set
+    fh2=figure('Name','Confusion matrix', 'MenuBar', 'none', 'NumberTitle', 'off');
+    if (~isempty(test_path))&(size(test_data,2)>cov_dim),
+        ztest=fhme(bnet2, nodes_info, test_d, size(test_d,1));
+        [Htest, testRate]=confmat(ztest, test_t);   % CM on the test set
+        subplot(1,2,1);
+    end
+    plotmat(Htrain,'b','k',12)
+    tick=[0.5:1:(0.5+nodes_info(2,end)-1)];
+    set(gca,'XTick',tick)
+    set(gca,'YTick',tick)
+    grid('off')
+    ylabel('True')
+    xlabel('Prediction')
+    title(['Confusion Matrix: training set (' num2str(trainRate(1)) '%)'])
+    if (~isempty(test_path))&(size(test_data,2)>cov_dim),
+        subplot(1,2,2)
+        plotmat(Htest,'b','k',12)
+        set(gca,'XTick',tick)
+        set(gca,'YTick',tick)
+        grid('off')
+        ylabel('True')
+        xlabel('Prediction')
+        title(['Confusion Matrix: test set (' num2str(testRate(1)) '%)'])
+    end
+    disp(' ')
+    disp('(Press any key to continue)');
+    pause
+end
+%-----------------------------------------------------------------------------------
+% Regression & Classification problem: calculate the predictions & plot the LL trace
+%-----------------------------------------------------------------------------------
+train_result=fhme(bnet2,nodes_info,train_d,size(train_d,1));
+if ~isempty(test_path),
+    test_result=fhme(bnet2,nodes_info,test_d,size(test_d,1));
+end
+fh3=figure('Name','Log-likelihood trace', 'MenuBar', 'none', 'NumberTitle', 'off')
+plot(LL2,'-ro',...
+                'MarkerEdgeColor','k',...
+                'MarkerFaceColor',[1 1 0],...
+                'MarkerSize',4)
+title('Log-likelihood trace')
+%-----------------------------------------------------------------------------------
+% Regression & Classification problem: save the predictions
+%-----------------------------------------------------------------------------------
+clc
+disp('------------------------------------------------------------------');
+disp('                           Save the results                       ');
+disp('------------------------------------------------------------------');
+disp(' ')
+%-----------------------------------------------------------------------------------
+save_quest_m=input('Do you want to save the HME model (Y/N)? [Y default]: ', 's');
+if isempty(save_quest_m),
+    save_quest_m='Y';
+end
+if ~findstr(save_quest_m, ['Y', 'N']), error('Invalid input'); end
+if save_quest_m=='Y',
+    disp(' ');
+    m_save=input('Insert the complete path for save the HME model (.mat):\n >> ', 's');
+    if isempty(m_save), error('You must specify a path!'); end
+    save(m_save, 'bnet2');
+end
+%-----------------------------------------------------------------------------------    
+disp(' ')
+save_quest=input('Do you want to save the HME predictions (Y/N)? [Y default]: ', 's');
+disp(' ')
+if isempty(save_quest),
+    save_quest='Y';
+end
+if ~findstr(save_quest, ['Y', 'N']), error('Invalid input'); end
+if save_quest=='Y',
+    tr_save=input('Insert the complete path for save the training data prediction (.mat):\n >> ', 's');    
+    if isempty(tr_save), error('You must specify a path!'); end
+    save(tr_save, 'train_result');  
+    if ~isempty(test_path),
+        disp(' ')
+        te_save=input('Insert the complete path for save the test data prediction (.mat):\n >> ', 's');
+        if isempty(te_save), error('You must specify a path!'); end
+        save(te_save, 'test_result');
+    end
+end
+clc
+disp('----------------------------------------------------');
+disp('                      B  Y  E !                     ');
+disp('----------------------------------------------------');
+pause(2)
+%clear 
+clc
diff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/test_data_class.mat b/sourcecodes/bnt-master/BNT/examples/static/HME/test_data_class.mat
new file mode 100644
index 00000000..7340c99e
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new file mode 100644
index 00000000..33b9aa43
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Binary files differdiff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/test_data_reg.mat b/sourcecodes/bnt-master/BNT/examples/static/HME/test_data_reg.mat
new file mode 100644
index 00000000..daffe218
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+++ b/sourcecodes/bnt-master/BNT/examples/static/HME/test_data_reg.mat
Binary files differdiff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/train_data_class.mat b/sourcecodes/bnt-master/BNT/examples/static/HME/train_data_class.mat
new file mode 100644
index 00000000..f181a433
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/HME/train_data_class.mat
Binary files differdiff --git a/sourcecodes/bnt-master/BNT/examples/static/HME/train_data_reg.mat b/sourcecodes/bnt-master/BNT/examples/static/HME/train_data_reg.mat
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+++ b/sourcecodes/bnt-master/BNT/examples/static/HME/train_data_reg.mat
Binary files differ