Add files via upload

Adding the *.m files used in parameter learning.

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diff --git a/BNW_parameter_learning/drawFigure.m b/BNW_parameter_learning/drawFigure.m
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+function [] = drawFigure(nnodes,bnet,labels,filename,cases,selectvar,selectdata)

+%drawFigure writes the parameters and data that are needed to draw the

+%structure of a Bayesian network.

+

+if nargin < 6,

+    drawFigureNoEv(nnodes,bnet,labels,filename,cases);

+else

+    drawFigureEv(nnodes,bnet,labels,filename,cases,selectvar,selectdata);

+end;

+

+end

+

+

+

+function [] = drawFigureEv(nnodes,bnet,labels,filename,cases,selectvar,selectdata)

+%Function to use if there is no entered evidence. 

+%         

+%

+%Before each printed line, I will have a line that starts with %%%

+% that describes what will be on that line

+

+%Create an empty evidence cell array.

+

+%val=cases;

+%for i = 1:nnodes

+% val(i,1)=val(i,2);

+

+%end

+

+A=cell2mat(cases');

+Amax=max(A);

+Amin=min(A);

+

+

+evidence = cell(1,nnodes);

+engine = jtree_inf_engine(bnet);

+

+evidence{selectvar}=selectdata;

+

+[engine,loglik]=enter_evidence(engine,evidence);

+

+%Open the file, and write the nodes to a file.

+fileID = fopen(filename,'w');

+

+%%%%Evidence node

+fprintf(fileID,'%i\n',selectvar);

+%%% The number of nodes

+fprintf(fileID,'%i\n',nnodes);

+%Get canvas size

+labels_temp = cellstr(labels);

+[x,y] = make_layout(bnet.dag);

+

+x = x - min(x);

+y = 1 - y;

+y = y - min(y);

+

+[x_dim,y_dim] = canvasSize(nnodes,x,y);

+

+%%% The dimensions of the canvas for the javascript code

+fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim)

+

+x = x*x_dim;

+y = y*y_dim;

+for i = 1:nnodes,

+%%% The name and X- and Y-positions of each node

+    fprintf(fileID,'%s\t%i\t%i\n',labels{i},round(x(i)),round(y(i)));

+end

+

+%Get the number of parents and children for each node.

+num_par = zeros(1,nnodes);

+%For parents, sum down columns

+for i = 1:nnodes,

+    for j = 1:nnodes,

+        if bnet.dag(j,i) == 1,

+            num_par(i) = num_par(i) + 1;

+        end

+    end

+end

+num_child = zeros(1,nnodes);

+for i = 1:nnodes,

+    for j = 1:nnodes,

+        if bnet.dag(i,j) == 1,

+            num_child(i) = num_child(i) + 1;

+        end

+    end

+end

+

+

+for i = 1:nnodes,

+    %%% The name and type of each node (1=continuous, the number of states

+    %%% if it is discrete

+    fprintf(fileID,'%s\t%i\n',labels{i},bnet.node_sizes(i));

+    %%% The size of the node, I am going to keep them 

+    %%% 250(width) by 150(height) for now

+    %Could modify this to change the width based on the length of the node

+    %name

+    fprintf(fileID,'%i\t%i\n',250,150);

+    %%% The number of parents of the node, and the parents

+    if num_par(i) == 0;

+        %%% If no parents:

+        fprintf(fileID,'%i\n',num_par(i));

+    else

+        parents = zeros(1,num_par(i));

+        k = 1;

+        for j = 1:nnodes,

+           if bnet.dag(j,i) == 1,

+             parents(1,k) = j;

+             k = k + 1;

+           end

+        end

+        format = '%i\t';

+        for j = 1:num_par(i)-1,

+            format = strcat(format,'%i\t');

+        end

+        format = strcat(format,'%i\n');

+        %%%If there are parents:

+        fprintf(fileID,format,num_par(i),parents(1,:));

+    end

+    

+    

+    %%% The number of children of the node, and the children

+    if num_child(i) == 0;

+        %%% If no children:

+        fprintf(fileID,'%i\n',num_child(i));

+    else

+        children = zeros(1,num_child(i));

+        k = 1;

+        for j = 1:nnodes,

+           if bnet.dag(i,j) == 1,

+             children(1,k) = j;

+             k = k + 1;

+           end

+        end

+        format = '%i\t';

+        for j = 1:num_child(i)-1,

+            format = strcat(format,'%i\t');

+        end

+        format = strcat(format,'%i\n');

+        %%%If there are parents:

+        fprintf(fileID,format,num_child(i),children(1,:));

+    end

+    

+    predict = marginal_nodes(engine,i);

+    if isempty(evidence{i})

+      if bnet.node_sizes(i) ~= 1,

+        for j = 1:bnet.node_sizes(i),

+            %%%For discrete nodes, the state and the percent of that state

+            fprintf(fileID,'%i\t%6.4f\n',j,predict.T(j));

+        end;

+      else

+

+        [x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i));

+        %%%For continuous nodes, print x and the pdf of a normal curve.

+        for j = 1:101,

+            fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1));

+        end;

+      end;

+    else

+      fprintf(fileID,'%6.4f\t%6.4f\n',selectdata,1);

+    end

+    

+end

+%fprintf(fileID,'%s\t %\n',labels_temp{:});

+

+

+fclose(fileID);

+

+end

+

+

+

+

+

+

+function [] = drawFigureNoEv(nnodes,bnet,labels,filename,cases)

+%Function to use if there is no entered evidence. 

+%         

+%

+%Before each printed line, I will have a line that starts with %%%

+% that describes what will be on that line

+A=cell2mat(cases');

+Amax=max(A);

+Amin=min(A);

+

+%Create an empty evidence cell array.

+evidence = cell(1,nnodes);

+engine = jtree_inf_engine(bnet);

+[engine,loglik] = enter_evidence(engine,evidence);

+

+%Open the file, and write the nodes to a file.

+fileID = fopen(filename,'w');

+%%% The number of nodes

+fprintf(fileID,'%i\n',nnodes);

+

+%Get canvas size

+

+labels_temp = cellstr(labels);

+[x,y] = make_layout(bnet.dag);

+%[x,y] = layout_dag(bnet.dag);

+

+

+x = x - min(x);

+y = 1 - y;

+y = y - min(y);

+

+[x_dim,y_dim] = canvasSize(nnodes,x,y);

+

+%%% The dimensions of the canvas for the javascript code

+fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim)

+

+x = x*x_dim;

+y = y*y_dim;

+for i = 1:nnodes,

+%%% The name and X- and Y-positions of each node

+    fprintf(fileID,'%s\t%i\t%i\n',labels{i},round(x(i)),round(y(i)));

+end

+

+%Get the number of parents and children for each node.

+num_par = zeros(1,nnodes);

+%For parents, sum down columns

+for i = 1:nnodes,

+    for j = 1:nnodes,

+        if bnet.dag(j,i) == 1,

+            num_par(i) = num_par(i) + 1;

+        end

+    end

+end

+num_child = zeros(1,nnodes);

+for i = 1:nnodes,

+    for j = 1:nnodes,

+        if bnet.dag(i,j) == 1,

+            num_child(i) = num_child(i) + 1;

+        end

+    end

+end

+

+

+for i = 1:nnodes,

+    %%% The name and type of each node (1=continuous, the number of states

+    %%% if it is discrete

+    fprintf(fileID,'%s\t%i\n',labels{i},bnet.node_sizes(i));

+    %%% The size of the node, I am going to keep them 

+    %%% 250(width) by 150(height) for now

+    %Could modify this to change the width based on the length of the node

+    %name

+    fprintf(fileID,'%i\t%i\n',250,150);

+    %%% The number of parents of the node, and the parents

+    if num_par(i) == 0;

+        %%% If no parents:

+        fprintf(fileID,'%i\n',num_par(i));

+    else

+        parents = zeros(1,num_par(i));

+        k = 1;

+        for j = 1:nnodes,

+           if bnet.dag(j,i) == 1,

+             parents(1,k) = j;

+             k = k + 1;

+           end

+        end

+        format = '%i\t';

+        for j = 1:num_par(i)-1,

+            format = strcat(format,'%i\t');

+        end

+        format = strcat(format,'%i\n');

+        %%%If there are parents:

+        fprintf(fileID,format,num_par(i),parents(1,:));

+    end

+    

+    

+    %%% The number of children of the node, and the children

+    if num_child(i) == 0;

+        %%% If no children:

+        fprintf(fileID,'%i\n',num_child(i));

+    else

+        children = zeros(1,num_child(i));

+        k = 1;

+        for j = 1:nnodes,

+           if bnet.dag(i,j) == 1,

+             children(1,k) = j;

+             k = k + 1;

+           end

+        end

+        format = '%i\t';

+        for j = 1:num_child(i)-1,

+            format = strcat(format,'%i\t');

+        end

+        format = strcat(format,'%i\n');

+        %%%If there are parents:

+        fprintf(fileID,format,num_child(i),children(1,:));

+    end

+    

+    predict = marginal_nodes(engine,i);

+    if bnet.node_sizes(i) ~= 1,

+        for j = 1:bnet.node_sizes(i),

+            %%%For discrete nodes, the state and the percent of that state

+            fprintf(fileID,'%i\t%6.4f\n',j,predict.T(j));

+        end;

+    else

+        %cases(i)

+       % MAX(cases(i))

+       % MIN(cases(i))

+        [x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i));

+        %%%For continuous nodes, print x and the pdf of a normal curve.

+        for j = 1:101,

+            fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1));

+        end;

+    end;

+end

+%fprintf(fileID,'%s\t %\n',labels_temp{:});

+

+

+fclose(fileID);

+

+end

+

+

+function [x_dim, y_dim] = canvasSize(nnodes,x,y)

+%canvasSize Function to calculate the size of the canvas to

+%           build the network structure

+

+

+%I am going to assume that the node size will be

+% height = 150, width = 250

+% so there will be a node spacing of 

+% 200 (in y-dim) and 300 (in x-dim).

+y_space = 200;

+x_space = 300;

+

+%Set default minimum x and y dimensions

+x_dim = 1200;

+y_dim = 1200;

+

+%get unique y values

+y_unique = unique(y);

+size_y = size(y_unique,2);

+y_dim_temp = size_y*y_space;

+

+%get the maximum nodes in any layer

+size_x = zeros(1,size_y);

+for i = 1:size_y,

+    for j = 1:nnodes,

+        if y_unique(i) == y(j),

+            size_x(1,i) = size_x(1,i) + 1;

+        end;

+    end;

+end;

+size_x = max(size_x);

+x_dim_temp = size_x*x_space;

+

+if x_dim_temp > x_dim,

+    x_dim = x_dim_temp;

+end;

+

+if y_dim_temp > y_dim,

+    y_dim = y_dim_temp;

+end;

+end

+

+function [x_vals,y_vals] = calcGaussian(mu,Sigma,maxval,minval)

+%Function to calculate 101 points of Gaussian function to use in plotting

+% Gets the probability density of the mean value and 50 evenly spaced

+% points up to 3Sigma below the mean and 50 evenly space points up to

+% 3Sigma above the mean.

+%maxval

+%minval

+x_vals = zeros(101,1);

+y_vals = zeros(101,1);

+

+%x_vals(1,1) = mu - 3*Sigma;

+x_vals(1,1) = minval - 1;

+gap=((maxval+1)-(minval - 1))/100;

+%x_vals(1,1) = 0;%mu - 3*Sigma;

+for i = 1:100,

+   % x_vals(i+1,1) = x_vals(1,1) + i*6*Sigma/100;

+    x_vals(i+1,1) = x_vals(i,1) + gap;

+ %x_vals(i+1,1) = x_vals(i,1) + 1/100;

+end

+

+for i = 1:101,

+    y_vals(i,1) = normpdf(x_vals(i,1),mu,Sigma);

+end

+

+end