Version 1.22

Adding new visualization options

1 files changed, 241 insertions, 0 deletions

diff --git a/sourcecodes/parameter_learning/code_backup/looCrossValid.m b/sourcecodes/parameter_learning/code_backup/looCrossValid.m
new file mode 100644
index 00000000..21d6560d
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+++ b/sourcecodes/parameter_learning/code_backup/looCrossValid.m
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+function looCrossValid(pre,predict_label)
+% This function will peform leave-one-out cross-validation.
+% This requires the specification of the name of the variable
+%   that you want to predict.
+% For now, I will assume that the variable is a discrete variable.
+
+
+
+sfile=strcat(pre,'structure_input.txt');
+dfile=strcat(pre,'continuous_input.txt');
+nnodefile=strcat(pre,'nnode.txt');
+fnnode = fopen(nnodefile,'r');
+nnodes = fscanf(fnnode,'%d');
+
+Std_flag=true;
+[labels,cases,bnet]=readInput(dfile,sfile,nnodes,Std_flag);
+
+for i=1:nnodes
+    if strcmp(labels(i),predict_label)
+       predict_node = i;
+    end
+end
+
+predict_cases = bnet.node_sizes(predict_node);
+
+if predict_cases == 1
+	looCV_continuous(pre,predict_label,nnodes,labels,cases,bnet,predict_node,predict_cases);
+else
+	looCV_discrete(pre,predict_label,nnodes,labels,cases,bnet,predict_node,predict_cases);
+endif
+
+end
+
+function looCV_continuous(pre,predict_label,nnodes,labels,cases,bnet,predict_node,predict_cases)
+
+ncases = size(cases,2);
+
+%%Read in original means and standard deviations to report output as
+%% untransformed values.
+means_orig=cell(1,nnodes);
+stdevs_orig=cell(1,nnodes);
+labels_orig=cell(1,nnodes);
+%Read in original means and standard deviations
+mapfile = strcat(pre,'map.txt');
+fmap = fopen(mapfile,'r');
+for i=1:nnodes
+    buffer = fgetl(mapfile);
+    temp = cell(1,3);
+    for j=1:3
+        [next,buffer] = strtok(buffer);
+	temp{j} = next;
+    end
+    labels_orig{i} = temp{1};
+    means_orig{i} = str2num(temp{3});
+    stdevs_orig{i} = str2num(temp{2});
+end
+fclose(fmap);
+%Need to map the means and stdevs to the correct labels
+means = cell(1,nnodes);
+stdevs = cell(1,nnodes);
+%Read in labels in new order.
+labelsnew = cell(1,nnodes);
+mapdatafile = strcat(pre,'mapdata.txt');
+fmapdata = fopen(mapdatafile,'r');
+buffer = fgetl(fmapdata);
+for i = 1:nnodes
+    [next,buffer ] = strtok(buffer);
+    labelsnew{i} = next;
+end
+fclose(fmapdata);
+for i = 1:nnodes
+    for j = 1:nnodes
+       if strcmp(labelsnew{i},labels_orig{j})
+          means{i} = means_orig{j};
+          stdevs{i} = stdevs_orig{j};
+          break
+       end
+    end
+end
+
+
+loopredictions=zeros(size(cases,2),2);
+
+%t=cputime;
+%First get loo predictions
+for i =1:ncases
+%  i
+  current_data = cases(:,i);
+  cases_new = cases;
+  cases_new(:,i) = [];
+  evidence = current_data;
+  evidence{predict_node} = {};
+  [bnet]=parameterLearning(bnet,cases_new);
+  engine = jtree_inf_engine(bnet);
+  [engine,loglik] = enter_evidence(engine,evidence);
+  predict = marginal_nodes(engine,predict_node);
+  adj_mu = predict.mu*stdevs{predict_node}+means{predict_node};
+  adj_sigma = stdevs{predict_node}*predict.Sigma;
+  loopredictions(i,1) = adj_mu;
+  loopredictions(i,2) = adj_sigma;
+end
+%e=cputime-t;
+
+
+%Open output file.
+filename = strcat(pre,'looCV.txt');
+fileID = fopen(filename,'w');
+
+fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
+
+
+%%Print the predictions
+fprintf(fileID,'Predicted mean and standard deviation for each case:\n');
+fprintf(fileID,'Mean\tStDev\n');
+for i = 1:ncases
+     fprintf(fileID,'%i\t',i);
+     fprintf(fileID,'%6.4f\t%6.4f\n',loopredictions(i,:));
+end
+
+end
+
+
+function looCV_discrete(pre,predict_label,nnodes,labels,cases,bnet,predict_node,predict_cases)
+
+ncases = size(cases,2);
+
+%This next section just gets the original names of the levels.
+% so they can be written to the output file.
+%%Get the maximum_number of states so array will be big enough
+%%Add 1 because the input includes the node name
+max_states = max(bnet.node_sizes) + 1;
+disc_nodes = size(bnet.dnodes,2);
+
+%%Get mapping of discrete levels.
+levelfile = strcat(pre,'nlevels.txt');
+flevels = fopen(levelfile,'r');
+levels = cell(disc_nodes,max_states);
+ndisc_nodes = 0;
+for i=1:disc_nodes
+    ndisc_nodes = ndisc_nodes + 1;
+    buffer = fgetl(flevels);
+     for j = 1:max_states
+       [next,buffer] = strtok(buffer);
+       if j == 1
+          levels{i,j} = next;
+       else
+%          levels{i,j} = uint16(str2num(next));
+          levels{i,j} = next;
+       end
+       if length(buffer) < 1
+        break
+       end
+     end
+end
+
+pred_levels = cell(1,predict_cases);
+for i = 1:disc_nodes
+   if strcmp(levels{i,1},predict_label);
+	for j = 1:predict_cases
+	    pred_levels{j} = levels{i,j+1};
+	end
+        break
+   end
+end
+
+loopredictions=zeros(size(cases,2),predict_cases);
+
+%t=cputime;
+%First get loo predictions
+for i =1:ncases
+%  i
+  current_data = cases(:,i);
+  cases_new = cases;
+  cases_new(:,i) = [];
+  evidence = current_data;
+  evidence{predict_node} = {};
+
+  [bnet]=parameterLearning(bnet,cases_new);
+  engine = jtree_inf_engine(bnet);
+  [engine,loglik] = enter_evidence(engine,evidence);
+  predict = marginal_nodes(engine,predict_node);
+  for j = 1:predict_cases
+	loopredictions(i,j) = predict.T(j);
+  end
+end
+%e=cputime-t;
+
+%Now compare with actual outcomes
+actual_states = zeros(1,predict_cases);
+for i=1:ncases
+    for j = 1:predict_cases
+	if cell2mat(cases(predict_node,i)) == j
+           actual_states(j) = actual_states(j) + 1;
+	end
+    end
+end
+
+actual_states;
+pred_states = zeros(1,ncases);
+
+for i=1:ncases
+    max_state = 1;
+    for j = 2:predict_cases
+       if loopredictions(i,j) > loopredictions(i,max_state)
+           max_state = j;
+	end
+    end
+    pred_states(i) = max_state;
+end
+
+correct = 0;
+for i=1:ncases
+    if pred_states(i) == cell2mat(cases(predict_node,i))
+	correct = correct + 1;
+    end
+end
+
+accuracy = correct/ncases;
+
+%Open output file.
+filename = strcat(pre,'looCV.txt');
+fileID = fopen(filename,'w');
+
+fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
+
+
+%%Print the accuracy
+fprintf(fileID,'Fraction of accurate predictions: %6.4f\n\n',accuracy);
+
+%%Print the predictions
+fprintf(fileID,'Predicted likelihood of each state for each case:\n');
+fprintf(fileID,'%s\t','Case');
+fprintf(fileID,'%s\t',pred_levels{1:end-1});
+fprintf(fileID,'%s\n',pred_levels{end});
+for i = 1:ncases
+     fprintf(fileID,'%i\t',i);
+     fprintf(fileID,'%6.4f\t',loopredictions(i,1:end-1));
+     fprintf(fileID,'%6.4f\n',loopredictions(i,end));
+end
+
+end