function RunLinearSVM(DataSetStartIndex, DataSetEndIndex, Method, RepType)

Methods = [cellstr('Random'), 'KShape'];

Types = [cellstr('Zexact'), 'Ztop5', 'Ztop10', 'Ztop20', 'Z99per', 'Z95per', 'Z90per', 'Z85per', 'Z80per'];

% first 2 values are '.' and '..' - UCR Archive 2018 version has 128 datasets

dir_struct = dir('/rigel/dsi/users/ikp2103/VLDBGRAIL/UCR2018/');

Datasets = {dir_struct(3:130).name};

% Sort Datasets

[Datasets, DSOrder] = sort(Datasets);

Results = zeros(length(Datasets),11);

addpath(genpath('LibLinear/matlab/.'));

%distcomp.feature( 'LocalUseMpiexec', false )

%rng(ceil(DataSetStartIndex*100))

%pause(30*rand);

%poolobj = gcp('nocreate');

�lete(poolobj);

%parpool(20);

rng('default')

for i = 1:length(Datasets)

if (i>=DataSetStartIndex && i<=DataSetEndIndex)

Results = zeros(length(Datasets),11);

disp(['Dataset being processed: ', char(Datasets(i))]);

DS = LoadUCRdataset(char(Datasets(i)));

[Thebestgamma,Thebestcost1,Thebestacc1,Thebestiming1] = GridSearchLinearSVM1(-10,1,20,DS.TrainInstancesCount,DS.TrainClassLabels,Datasets,i,Methods, Method, Types, RepType);

[Thebestcost2,Thebestacc2,Thebestiming2] = GridSearchLinearSVM2(-10,0.1,20,DS.TrainInstancesCount,DS.TrainClassLabels,Datasets,i,Methods, Method, Types, RepType,Thebestgamma);

rep = 1;

ZRep = dlmread( strcat( 'REPRESENTATIONSGamma', num2str(Thebestgamma),'/',char(Datasets(i)),'/','RepLearningFixedSamples', '_', char(Methods(Method)), '_', num2str(rep) ,'.',char(Types(RepType))) );

ZRepTrain = ZRep(1:DS.TrainInstancesCount,:);

ZRepTest = ZRep(DS.TrainInstancesCount 1:end,:);

ZRepTrain = sparse(ZRepTrain);

ZRepTest = sparse(ZRepTest);

tic;

cmd = ['-e 0.001 -s 2 -c ', num2str(2^Thebestcost2)];

model_precomputed = train(DS.TrainClassLabels, ZRepTrain, cmd);

ModelTrainingRuntime = toc;

tic;

[predict_label_P, accuracy_P, dec_values_P] = predict(DS.TestClassLabels, ZRepTest, model_precomputed);

PredictionRuntime = toc;

Results(i,1) = Thebestgamma;

Results(i,2) = Thebestcost1;

Results(i,3) = Thebestcost2;

%Results(i,4) = Thebestcost3;

Results(i,4) = 0;

Results(i,5) = Thebestacc1*0.01;

Results(i,6) = Thebestacc2*0.01;

%Results(i,7) = Thebestacc3*0.01;

Results(i,7) = 0;

%Results(i,8) = Thebestiming1 Thebestiming2 Thebestiming3;

Results(i,8) = Thebestiming1 Thebestiming2;

Results(i,9) = accuracy_P(1)*0.01;

Results(i,10) = ModelTrainingRuntime;

Results(i,11) = PredictionRuntime;

dlmwrite( strcat('RunLinearSVM/','RunLinearSVM_', char(Methods(Method)), '_', char(Types(RepType)), '_Dataset_', num2str(i)) , Results, 'delimiter', '\t');

end

end

%poolobj = gcp('nocreate');

�lete(poolobj);

end

function [Thebestgamma,Thebestcost,Thebestacc,Thebestiming] = GridSearchLinearSVM1(GridStart,GridStep,GridEnd,TrainInstancesCount,TrainClassLabels,Datasets,DatasetsNumber,Methods, Method, Types, RepType)

rep=1;

previousMaxbestacc = 0;

Thebestgamma = 0;

Thebestcost = 0;

Thebestacc = 0;

Thebestiming = 0;

% Tuning Parameters

for gamma=1:20

gamma

log2cTmp = GridStart:GridStep:GridEnd;

bestacc = zeros(1,length(log2cTmp));

bestgamma = zeros(1,length(log2cTmp));

bestcost = zeros(1,length(log2cTmp));

besttiming = zeros(1,length(log2cTmp));

ZRep = dlmread( strcat( 'REPRESENTATIONSGamma', num2str(gamma),'/',char(Datasets(DatasetsNumber)),'/','RepLearningFixedSamples', '_', char(Methods(Method)), '_', num2str(rep) ,'.',char(Types(RepType))) );

ZRepTrain = ZRep(1:TrainInstancesCount,:);

ZRepTrain = sparse(ZRepTrain);

% grid search

for log2cNEW = 1:length(log2cTmp)

log2cNEW

tic;

log2c = log2cTmp(log2cNEW);

cmd = ['-q -e 0.001 -s 2 -v ' num2str(10) ' -c ', num2str(2^log2c)];

cv = train(TrainClassLabels, ZRepTrain, cmd);

bestacc(log2cNEW) = cv;

bestcost(log2cNEW) = log2c;

bestgamma(log2cNEW) = gamma;

besttiming(log2cNEW) = toc;

end

[Maxbestacc,~] = max(bestacc);

Posbestacc = find(bestacc==Maxbestacc,1,'last');

Thebestiming = Thebestiming sum(besttiming);

if Maxbestacc>previousMaxbestacc

Thebestgamma = bestgamma(Posbestacc);

Thebestcost = bestcost(Posbestacc);

Thebestacc = Maxbestacc;

previousMaxbestacc = Maxbestacc;

end

end

end

function [Thebestcost,Thebestacc,Thebestiming] = GridSearchLinearSVM2(GridStart,GridStep,GridEnd,TrainInstancesCount,TrainClassLabels,Datasets,DatasetsNumber,Methods, Method, Types, RepType,Thebestgamma)

rep=1;

% Tuning Parameters

log2cTmp = GridStart:GridStep:GridEnd;

bestacc = zeros(1,length(log2cTmp));

bestcost = zeros(1,length(log2cTmp));

besttiming = zeros(1,length(log2cTmp));

ZRep = dlmread( strcat( 'REPRESENTATIONSGamma', num2str(Thebestgamma),'/',char(Datasets(DatasetsNumber)),'/','RepLearningFixedSamples', '_', char(Methods(Method)), '_', num2str(rep) ,'.',char(Types(RepType))) );

ZRepTrain = ZRep(1:TrainInstancesCount,:);

ZRepTrain = sparse(ZRepTrain);

% grid search

for log2cNEW = 1:length(log2cTmp)

log2cNEW

tic;

log2c = log2cTmp(log2cNEW);

cmd = ['-q -e 0.001 -s 2 -v ' num2str(10) ' -c ', num2str(2^log2c)];

cv = train(TrainClassLabels, ZRepTrain, cmd);

bestacc(log2cNEW) = cv;

bestcost(log2cNEW) = log2c;

besttiming(log2cNEW) = toc;

end

[Maxbestacc,~] = max(bestacc);

Posbestacc = find(bestacc==Maxbestacc,1,'last');

Thebestiming = sum(besttiming);

Thebestcost = bestcost(Posbestacc);

Thebestacc = Maxbestacc;

end