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clear; clc; close all;
addpath('utility');
addpath('ElasticNet');
addpath('ElasticNet/EN');
addpath('ElasticNet/SSEN');
addpath('ElasticNet/LapEN');
addpath('ElasticNet/imm');
%% parameters
dataset_id = 4;
TrainNum = [10:5:50];
nRepeat = 10;
dataset_subset = 'subset_index/subset_ucsd_FacDiv.mat';
feature = 'all';
%% load dataset
[trainFrame, testFrame, dataset_feature, dataset_ground_truth] = getDataset(dataset_id);
feaIndex = getFeatureIndex(feature, dataset_id);
load(dataset_feature);
load(dataset_ground_truth);
load(dataset_subset);
Train.Feature = Feature(trainFrame,feaIndex);
Train.Truth = count(trainFrame);
Test.Feature = Feature(testFrame,feaIndex);
Test.Truth = count(testFrame);
nTrain = size(Train.Feature,1);
nTest = size(Test.Feature,1);
%% evaluation
% elastic net
% warning off;
% for iTrain=1:length(TrainNum)
% for iIndex = 1:size(DataIndex,1)
% labelSet = DataIndex(iIndex,1:TrainNum(iTrain));
% Label.Feature = Train.Feature(labelSet,:);
% Label.Truth = Train.Truth(labelSet);
% % normalization
% Xmean = mean(Train.Feature,1); Xstd = std(Train.Feature,1);
% Ymean = mean(Label.Truth);
% TrainFeature = (Train.Feature - repmat(Xmean,nTrain,1))./repmat(Xstd,nTrain,1);
% TestFeature = (Test.Feature - repmat(Xmean,nTest,1))./repmat(Xstd,nTest,1);
% LabelTruth = Label.Truth - Ymean;
% % Elastic Net
% params = EN0(TrainFeature, labelSet, LabelTruth, TestFeature, Test.Truth, Ymean);
% predict_raw = TestFeature * params.opt_beta' + Ymean;
% predict = max(round(predict_raw),0);
% mae_en(iTrain, iIndex) = mean(abs(predict - Test.Truth));
% mse_en(iTrain, iIndex) = mean((predict - Test.Truth).^2);
% % Elastic Net
% % params = EN2(TrainFeature, labelSet, LabelTruth);
% % predict_raw = TestFeature * params.opt_beta' + Ymean;
% % predict = max(round(predict_raw),0);
% % mae_en(iTrain) = mean(abs(predict - Test.Truth));
% % mse_en(iTrain) = mean((predict - Test.Truth).^2);
% end
% end
% semi supervised elastic net
% warning off;
% mae_ssen = zeros(length(TrainNum),size(DataIndex,1));
% mse_ssen = zeros(length(TrainNum),size(DataIndex,1));
% for iTrain=1:length(TrainNum)
% for iIndex = 1:size(DataIndex,1)
% labelSet = DataIndex(iIndex, 1:TrainNum(iTrain));
% Label.Feature = Train.Feature(labelSet,:);
% Label.Truth = Train.Truth(labelSet);
% % normalization
% Xmean = mean(Train.Feature,1); Xstd = std(Train.Feature,1);
% Ymean = mean(Label.Truth);
% TrainFeature = (Train.Feature - repmat(Xmean,nTrain,1))./repmat(Xstd,nTrain,1);
% TestFeature = (Test.Feature - repmat(Xmean,nTest,1))./repmat(Xstd,nTest,1);
% LabelTruth = Label.Truth - Ymean;
% % Semi-supervised Elastic Net
% mae_table = zeros(nRepeat,1);
% mse_table = zeros(nRepeat,1);
% for iRep = 1:nRepeat
% params = SSLasso2(TrainFeature, labelSet, LabelTruth);
% predict_raw = TestFeature * params.opt_beta' + Ymean;
% predict = max(round(predict_raw),0);
% % mae_ssen(iTrain, iRep) = mean(abs(predict - Test.Truth));
% % mse_ssen(iTrain, iRep) = mean((predict - Test.Truth).^2);
% mae_table(iRep) = mean(abs(predict - Test.Truth));
% mse_table(iRep) = mean((predict - Test.Truth).^2);
% % [iTrain iRep mae_ssen(iTrain, iRep) mse_ssen(iTrain, iRep)]
% [iTrain iRep mae_table(iRep) mse_table(iRep)]
% end
% params = SSLasso2(TrainFeature, labelSet, LabelTruth);
% predict_raw = TestFeature * params.opt_beta' + Ymean;
% predict = max(round(predict_raw),0);
% mae_ssen(iTrain,iIndex) = mean(abs(predict - Test.Truth));
% mse_ssen(iTrain),iIndex = mean((predict - Test.Truth).^2);
% [mae_ssen(iTrain,iIndex) mse_ssen(iTrain,iIndex) params.lambda2 params.lambda3]
% end
% end
% % semi-supervised elastic net (parallel)
% % matlabpool 9
% tic
% spmd
% iTrain = labindex;
% warning off;
% mae_table = zeros(nRepeat,size(DataIndex,1));
% mse_table = zeros(nRepeat,size(DataIndex,1));
% % for iIndex = 1:size(DataIndex,1)
% for iIndex = subIndex
% [iTrain iIndex]
% labelSet = DataIndex(iIndex, 1:TrainNum(iTrain));
% Label.Feature = Train.Feature(labelSet,:);
% Label.Truth = Train.Truth(labelSet);
% % normalization
% Xmean = mean(Train.Feature,1); Xstd = std(Train.Feature,1);
% Ymean = mean(Label.Truth);
% TrainFeature = (Train.Feature - repmat(Xmean,nTrain,1))./repmat(Xstd,nTrain,1);
% TestFeature = (Test.Feature - repmat(Xmean,nTest,1))./repmat(Xstd,nTest,1);
% LabelTruth = Label.Truth - Ymean;
% for iRep = 1:nRepeat
% params = SSLasso2(TrainFeature, labelSet, LabelTruth);
% predict_raw = TestFeature * params.opt_beta' + Ymean;
% predict = max(round(predict_raw),0);
% mae_table(iRep,iIndex) = mean(abs(predict - Test.Truth));
% mse_table(iRep,iIndex) = mean((predict - Test.Truth).^2);
% end
% end
% mae_mean = mean(mae_table,1); mae_std = std(mae_table,1);
% mse_mean = mean(mse_table,1); mse_std = std(mse_table,1);
% end
% MAE.mean = zeros(length(TrainNum),size(DataIndex,1));
% MSE.mean = zeros(length(TrainNum),size(DataIndex,1));
% MAE.std = zeros(length(TrainNum),size(DataIndex,1));
% MSE.std = zeros(length(TrainNum),size(DataIndex,1));
% for i=1:length(TrainNum)
% MAE.mean(i,:) = mae_mean{i};
% MSE.mean(i,:) = mse_mean{i};
% MAE.std(i,:) = mae_std{i};
% MSE.std(i,:) = mse_std{i};
% end
% toc
% Laplacian semi-supervised elastic net (parallel)
% matlabpool 9
tic
spmd
iTrain = labindex;
warning off;
mae_table = zeros(nRepeat,size(DataIndex,1));
mse_table = zeros(nRepeat,size(DataIndex,1));
for iIndex = 1:size(DataIndex,1)
[iTrain iIndex]
labelSet = DataIndex(iIndex, 1:TrainNum(iTrain));
Label.Feature = Train.Feature(labelSet,:);
Label.Truth = Train.Truth(labelSet);
% normalization
Xmean = mean(Train.Feature,1); Xstd = std(Train.Feature,1);
Ymean = mean(Label.Truth);
TrainFeature = (Train.Feature - repmat(Xmean,nTrain,1))./repmat(Xstd,nTrain,1);
TestFeature = (Test.Feature - repmat(Xmean,nTest,1))./repmat(Xstd,nTest,1);
LabelTruth = Label.Truth - Ymean;
% evaluation
for iRep = 1:nRepeat
para = LapEN1(TrainFeature, labelSet, LabelTruth, params(iIndex).similarity, params(iIndex).cluster);
predict_raw = TestFeature * para.opt_beta' + Ymean;
predict = max(round(predict_raw),0);
mae_table(iRep,iIndex) = mean(abs(predict - Test.Truth));
mse_table(iRep,iIndex) = mean((predict - Test.Truth).^2);
end
end
mae_mean = mean(mae_table,1); mae_std = std(mae_table,1);
mse_mean = mean(mse_table,1); mse_std = std(mse_table,1);
end
MAE.mean = zeros(length(TrainNum),size(DataIndex,1));
MSE.mean = zeros(length(TrainNum),size(DataIndex,1));
MAE.std = zeros(length(TrainNum),size(DataIndex,1));
MSE.std = zeros(length(TrainNum),size(DataIndex,1));
for i=1:length(TrainNum)
MAE.mean(i,:) = mae_mean{i};
MSE.mean(i,:) = mse_mean{i};
MAE.std(i,:) = mae_std{i};
MSE.std(i,:) = mse_std{i};
end
toc
save result_LapEN_ucsd