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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
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