MAP

function Exp_Tutorial_1(isMasterNode)

% Some description of the experiment goes here

%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Set up the basic folders

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

expName = 'Tutorial';

dataFolderPrefix = 'featR';

if isunix

    expFolderPrefix = '/scratch/nrclark/exps/';

else

    expFolderPrefix = 'D:\Exps';

end

expFolder = fullfile(expFolderPrefix,expName);

hmmFolder = fullfile(expFolder,'hmm');

%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Sort out the training (LEARNING) condition

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

learnFolder = fullfile(expFolder,'featL');

xL = cJob('L', learnFolder);

xL.participant = 'Normal';

xL.MAPparamChanges= {'DRNLParams.rateToAttenuationFactorProb=0;', 'OMEParams.rateToAttenuationFactorProb=0;' };

xL.noiseLevToUse   =  -200;

xL.speechLevToUse  =  60;

xL.MAPopHSR = 1;

xL.MAPopMSR = 0;

xL.MAPopLSR = 0;

xL.numCoeff = 14;

xL.removeEnergyStatic = 0;

%%%%% Group of params that will influence simulation run time %%%%%%%

xL.numWavs = 12; %MAX=8440

testWavs = 6; %MAX = 358

nzLevel = [-200 40:10:70];

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

xL.noisePreDur = 1;

xL.noisePostDur = 0.1;

xL.truncateDur  = xL.noisePreDur-0.1;

xL.noiseName = 'pink_demo';

if isMasterNode && ~isdir(xL.opFolder)

    mkdir(xL.opFolder);

    xL = xL.assignFiles;

    xL.storeSelf; % This is a call to a member function and is not a pointless line of code!

end

%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Sort out the testing (RECOGNITION) conditions

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

recConditions = numel(nzLevel);

tmpIdx=0;

for nn = 0*recConditions+1:1*recConditions

    tmpIdx=tmpIdx+1;

    xR{nn} = xL; %simply copy the "Learn" object and change it a bit below

    recFolder = fullfile(expFolder,[dataFolderPrefix num2str(nn)]);

    xR{nn}.opFolder = recFolder;

    %These are the interesting differences between training and testing

    xR{nn}.numWavs = testWavs; %MAX = 358

    xR{nn}.noiseLevToUse = nzLevel(tmpIdx);

    xR{nn}.MAPparamChanges= {'DRNLParams.rateToAttenuationFactorProb=0;'};

    %Now just to wrap it up ready for processing

    if isMasterNode && ~isdir(xR{nn}.opFolder)

        mkdir(xR{nn}.opFolder);

        xR{nn} = xR{nn}.assignWavPaths('R');

        xR{nn} = xR{nn}.assignFiles;

        xR{nn}.storeSelf;

    end

end

tmpIdx=0;

for nn = 1*recConditions+1:2*recConditions

    tmpIdx=tmpIdx+1;

    xR{nn} = xL; %simply copy the "Learn" object and change it a bit below

    recFolder = fullfile(expFolder,[dataFolderPrefix num2str(nn)]);

    xR{nn}.opFolder = recFolder;

    %These are the interesting differences between training and testing

    xR{nn}.numWavs = testWavs; %MAX = 358

    xR{nn}.noiseLevToUse = nzLevel(tmpIdx);

    xR{nn}.MAPparamChanges= {'DRNLParams.rateToAttenuationFactorProb=-10^(-10/20);'};

    %Now just to wrap it up ready for processing

    if isMasterNode && ~isdir(xR{nn}.opFolder)

        mkdir(xR{nn}.opFolder);

        xR{nn} = xR{nn}.assignWavPaths('R');

        xR{nn} = xR{nn}.assignFiles;

        xR{nn}.storeSelf;

    end

end

%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% ** Generate features **

% This is the time consuming, processing intensive portion of the program.

% Nodes that are not the master node are only interested in the opFolder

% member of the jobjects.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

worker(xL.opFolder);

maxConds = nn;

if ~isMasterNode %dont bother wasting master node effort on generating testing features (for now)

    for nn = 1:maxConds

        worker(xR{nn}.opFolder);

    end

end

%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Train and test the recogniser - a job for the master node only

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

if isMasterNode

    while(~all(xL.todoStatus==2))

        disp('Waiting on straggler nodes to complete their jobs before HMM is trained . . .')

        pause(30); %Wait for 30 seconds before looking again

        xL.lockJobList;

        xL = xL.loadSelf; %Reload incase changed

        xL.unlockJobList;

    end

    y = cHMM(hmmFolder);

    y.numCoeff = (xL.numCoeff-logical(xL.removeEnergyStatic)) * 3;

    y.createSCP(xL.opFolder)

    y.createMLF(xL.opFolder)

    y.train(xL.opFolder) %This node can be busy training, even if other jobs are being processed for testing

    % ALLOW MASTER NODE TO MUCK IN WITH GENERATING TESTING FEATURES ONCE

    % HMM HAS BEEN TRAINED

    for nn = 1:maxConds

        worker(xR{nn}.opFolder);

    end

    xR{end}.lockJobList;

    xR{end} = xR{end}.loadSelf; %Reload changes

    xR{end}.unlockJobList;

    while(~all(xR{end}.todoStatus==2))

        disp('Waiting on straggler nodes to complete their jobs before HMM is tested . . .')

        pause(30); %Wait for 30 seconds before looking again

        xR{end}.lockJobList;

        xR{end} = xR{end}.loadSelf; %Reload incase changed

        xR{end}.unlockJobList;

    end

    for nn = 1:maxConds

        y.createSCP(xR{nn}.opFolder);

        y.test(xR{nn}.opFolder);

    end

    %Show all of the scores in the command window at the end

    for nn = 1:maxConds

        y.score(xR{nn}.opFolder);

    end

end