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Diffstat (limited to 'BNW_parameter_learning/drawFigureM.m')
| -rw-r--r-- | BNW_parameter_learning/drawFigureM.m | 230 |
1 files changed, 230 insertions, 0 deletions
diff --git a/BNW_parameter_learning/drawFigureM.m b/BNW_parameter_learning/drawFigureM.m new file mode 100644 index 00000000..9aa77d35 --- /dev/null +++ b/BNW_parameter_learning/drawFigureM.m @@ -0,0 +1,230 @@ +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 |