1 files changed, 0 insertions, 219 deletions
diff --git a/BNW_parameter_learning/drawFigure.m b/BNW_parameter_learning/drawFigure.m
deleted file mode 100644
index 599d8f3b..00000000
--- a/BNW_parameter_learning/drawFigure.m
+++ /dev/null
@@ -1,219 +0,0 @@
-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