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% clear all
proto_model={'T_h1_m1';'S_h1_m1';'T_h1_m2';'S_h1_m2';'T_h2_m1';'S_h2_m1'};
nmb_of_proto_models=length(proto_model);
featured_model={'model_1';'model_2';'model_3';'model_4';'model_5';'model_6'};
[nmb_of_ft_models,~]=size(featured_model);
nmb_of_lab=1000;
nmb_of_batches=10;
nmb_of_image_set=zeros(1,10);
pr_set=zeros(1,10);
model_accuracy_comparison=zeros(1,nmb_of_ft_models);
pm.nmb_of_lab=nmb_of_lab;
edges=1:1:(nmb_of_lab+1);
% top_n_labels=10;
confusion_matrixes=zeros(nmb_of_lab,nmb_of_lab,nmb_of_batches,nmb_of_ft_models,nmb_of_proto_models);
model_c_matrixes=zeros(nmb_of_lab,nmb_of_lab,nmb_of_ft_models,nmb_of_proto_models);
for ii=1:nmb_of_proto_models
md=char(proto_model(ii));
% confusion_matrixes=zeros(nmb_of_lab,nmb_of_lab,nmb_of_batches,nmb_of_ft_models);
% model_c_matrixes=zeros(nmb_of_lab,nmb_of_lab,nmb_of_ft_models);
top_100_pr_by_batch=zeros(nmb_of_batches,nmb_of_ft_models); % 100 postive rate
top_100_pr_by_model=zeros(1,nmb_of_ft_models); % 100 postive rate
batch_nmbs=zeros(1,nmb_of_batches);
for fm=1:nmb_of_ft_models
top_100_accnt=[];
c_matrix=zeros(nmb_of_lab,nmb_of_lab);
for imgnt1kdataset=1:nmb_of_batches
reportname1 = sprintf('Model_%s/Evaluation_Data/Model_Accuracy/training_data_batch_%d_feature_module_performance_%s_var.mat', md,imgnt1kdataset,md);
aa=sprintf('classification_data_%d',fm);
bb=load(reportname1,aa);
c_data=bb.(aa);
true_lab=c_data(:,1);
pred_lab=c_data(:,2);
nmb_of_data=length(true_lab);
c_mtx_output=fun_confusion_matrix(true_lab,pred_lab,pm);
bb=c_mtx_output.conf_matrix;
confusion_matrixes(:,:,imgnt1kdataset,fm,ii)=bb;
c_matrix=c_matrix+nmb_of_data*bb;
top_100_pr_by_batch(imgnt1kdataset,fm)=length(c_mtx_output.lab_100);
top_100_accnt=[top_100_accnt,c_mtx_output.lab_100];
batch_nmbs(imgnt1kdataset)=nmb_of_data;
end
model_c_matrixes(:,:,fm,ii)=c_matrix/sum(batch_nmbs);
histN = histcounts(top_100_accnt,edges);
idx=(histN==nmb_of_batches);
top_100_pr_by_model(fm)=sum(1*idx);
end
% mean(top_100_pr_by_batch,1) % average 100-rate by data batch
% top_100_pr_by_model
%%%%%%%
assignin('base',md, [mean(top_100_pr_by_batch,1);top_100_pr_by_model]')
end
%%
table(featured_model,T_h1_m1,T_h1_m2,T_h2_m1)
%%
% Find the labels that are perfectly classified, by model
%
ii=1;
fm=6;
lab=1:1000;
A=model_c_matrixes(:,:,fm,ii);
dd=diag(A,0);
idx=(dd==100);
lab_100=lab(idx)
nmb_of_lab_100=sum(1*idx)
%%
% Find the labels that are perfectly classified, by batch and model
%
ii=1;
fm=6;
imgnt1kdataset=1;
lab=1:1000;
A=confusion_matrixes(:,:,imgnt1kdataset,fm,ii);
dd=diag(A,0);
idx=(dd==100);
lab_100=lab(idx)
nmb_of_lab_100=sum(1*idx)
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