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function [] = drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means)
%drawFigure writes the parameters and data that are needed to draw the
%structure of a Bayesian network for BNW.
% This is the function that is called to create the initial
% net_figure file for the network (before evidence or intervention).
%
%
% The output is the file specified by 'filename'.
% For BNW, this file is called: ???net_figure.txt
% where ??? is the prefix.
%
% drawFigure is called by runBN_intial.m
%
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,
%%Undo standardization
x_vals(j,1) = x_vals(j,1)*stdevs{i}+means{i};
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
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