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Diffstat (limited to 'sourcecodes/parameter_learning/code_backup/looCrossValid.m')
| -rw-r--r-- | sourcecodes/parameter_learning/code_backup/looCrossValid.m | 241 |
1 files changed, 0 insertions, 241 deletions
diff --git a/sourcecodes/parameter_learning/code_backup/looCrossValid.m b/sourcecodes/parameter_learning/code_backup/looCrossValid.m deleted file mode 100644 index 21d6560d..00000000 --- a/sourcecodes/parameter_learning/code_backup/looCrossValid.m +++ /dev/null @@ -1,241 +0,0 @@ -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 |