Version 1.22

Adding new visualization options

1 files changed, 230 insertions, 0 deletions

diff --git a/sourcecodes/parameter_learning/code_backup/drawFigureM.m b/sourcecodes/parameter_learning/code_backup/drawFigureM.m
new file mode 100644
index 00000000..91b8698f
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+++ b/sourcecodes/parameter_learning/code_backup/drawFigureM.m
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+function [] = drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata)
+%drawFigureM writes the parameters and data that are needed to draw the
+%structure of a Bayesian network after added evidence or intervention
+
+fileID = fopen(filename,'w');
+
+
+A=cell2mat(cases');
+Amax=max(A);
+Amin=min(A);
+
+
+evidence = cell(1,nnodes);
+engine = jtree_inf_engine(bnet);
+
+m = size(selectvar,1);
+
+ev_dat = zeros(1,nnodes);
+for i = 1:m,
+    di=selectvar(i,1);
+    ev_dat(di)=selectdata(i,1);   
+%Need to standardized evidence for continuous nodes.
+    if bnet.node_sizes(di) == 1
+        ev_dat(di) = (ev_dat(di) - means{di}) / stdevs{di};
+    end
+    evidence{di}=ev_dat(di);
+    fprintf(fileID,'%i\t',di);
+end
+
+fprintf(fileID,'\n');
+
+[engine,loglik]=enter_evidence(engine,evidence);
+
+%Open the file, and write the nodes to a file.
+%%% 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,
+            %%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;
+    else
+      if bnet.node_sizes(i) == 1,
+	fprintf(fileID,'%6.4f\t%6.4f\n',ev_dat(i)*stdevs{i}+means{i},1);
+      else
+	fprintf(fileID,'%6.4f\t%6.4f\n',ev_dat(i),1);
+      endif 
+    end
+    
+end
+
+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