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DesignAnn.m
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DesignAnn.m
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function ANN = DesignAnn (AnnSelections, Parameters)
% Design ANNs using the built in training systems to detect APs at
% particular velocities.
%
% C.T.Clarke based on the work of Assad al Shueli
% Edited by L F Tiong 06/05/2016
% Edited by A Vlissidis 06/05/2017
%
% Use a parameter structure like this:
%
% Parameters = struct ( ...
% 'Electrodes', 11 , ...
% 'ElectrodeSpacing', 0.003, ...
% 'SamplingFrequency', 100000 , ...
% 'ActionPotentialType', 'long' , ...
% 'StartTestVelocity', 1 , ...
% 'StepTestVelocity', 1 , ...
% 'EndTestVelocity', 100 , ...
% 'NoiseLevel', 0.01 );
%
% Other parameters may be included but will be ignored
% Load all ANNs which have been stored as array called
% ANN(i).net where i is the ANN index that includes all
% the values in AnnSelections below.
% Get the number of ANNs to train
NumAnns = numel(AnnSelections);
% Pre-allocate for speed
ANN(1:NumAnns) = struct('net', []);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Setup required variables
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Handle to set action potential type
if strcmp(Parameters.APType, 'UniPolar')
GetData = @GetUniPolar;
DataLines = Parameters.Electrodes;
elseif strcmp(Parameters.APType, 'TriPolar')
GetData = @GetTriPolar;
DataLines = Parameters.Electrodes - 2;
elseif strcmp(Parameters.APType, 'BiPolar')
GetData = @GetBiPolar;
DataLines = floor(Parameters.Electrodes/2);
end
% Set up the time sequence for the signals at each velocity
InitTime = 0.001;
EndTime = 0.004;
Time = -InitTime:1/Parameters.SamplingFrequency:EndTime;
TimeLength = numel(Time);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Design each ANN
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for AnnIndex=1:NumAnns
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Create input signals for training
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Retrieve the matched velocity of the selected ANN
MatchedVelocity = AnnSelections(AnnIndex);
% Set the initial Velocity set including the repeated matched set at
% the end that match the ANN's target velocity
Velocities = cat(2,Parameters.StartTestVelocity: ...
Parameters.StepTestVelocity:Parameters.EndTestVelocity, ...
MatchedVelocity*ones(1,Parameters.MatchRepeats));
NumVelocities = numel(Velocities);
% Set up the array to hold the input training data
TrainingInputSequence = zeros(DataLines, ...
NumVelocities * TimeLength);
% Create an input sequence for the ANN
for VelocityIndex = 1:NumVelocities
% Retrieve the selected velocity
Velocity = Velocities(VelocityIndex);
% Get a data set of the appropriate velocity
Data = GetData(Parameters, Velocity, Time);
% Insert the data into the input sequence
InsertStart = 1 + (VelocityIndex - 1) * TimeLength;
InsertEnd = InsertStart + TimeLength - 1;
TrainingInputSequence (:,InsertStart:InsertEnd) = Data;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Create an output target signal for training
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Clear the target
TrainingTargetSequence = zeros(1,NumVelocities*TimeLength);
% Add a pulse for each velocity that is the same as the matched
% velocity
VelocityIndices = 1:NumVelocities;
for VelocityIndex = VelocityIndices(Velocities == MatchedVelocity)
% Get the insertion point for the data in the input sequence
InsertStart = 1 + (VelocityIndex - 1) * TimeLength;
InsertEnd = InsertStart + TimeLength - 1;
% Get a data set of the appropriate velocity with no noise
NoiseLessParameters = Parameters;
NoiseLessParameters.NoiseLevel = 0;
Data = GetData(NoiseLessParameters, MatchedVelocity, Time);
% Get the final channel
FinalChannel = Data(DataLines,:);
% Normalise it
FinalChannel = AgcSim(FinalChannel);
switch lower(Parameters.TargetOutput)
case 'pulse'
% Create a pulse at the 3dB points
TargetPulse = zeros(size(FinalChannel));
TargetPulse(FinalChannel >= (max(FinalChannel)/sqrt(2))) = 1;
% Apply it to the target sequence
TrainingTargetSequence(InsertStart:InsertEnd) = TargetPulse;
case 'ap'
% Target sequence is action potential with matched velocity
TrainingTargetSequence(InsertStart:InsertEnd) = FinalChannel;
otherwise
disp('Unknown output training target type.')
end;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Create input signals for validation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%2e-21;
valParameters = Parameters;
valParameters.StepTestVelocity = 5;
valParameters.NoiseLevel = 2e-22;
valParameters.MatchRepeats = 5;
% Set the validation Velocity set including the repeated matched set at
% the end that match the ANN's target velocity
Velocities = cat(2,valParameters.StartTestVelocity: ...
valParameters.StepTestVelocity:valParameters.EndTestVelocity, ...
MatchedVelocity*ones(1,valParameters.MatchRepeats));
NumVelocities = numel(Velocities);
% Set up the array to hold the input training data
ValidationInputSequence = zeros(DataLines, ...
NumVelocities * TimeLength);
% Create an input sequence for the ANN
for VelocityIndex = 1:NumVelocities
% Retrieve the selected velocity
Velocity = Velocities(VelocityIndex);
% Get a data set of the appropriate velocity
Data = GetData(valParameters, Velocity, Time);
% Insert the data into the input sequence
InsertStart = 1 + (VelocityIndex - 1) * TimeLength;
InsertEnd = InsertStart + TimeLength - 1;
ValidationInputSequence (:,InsertStart:InsertEnd) = Data;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Create an output target signal for validation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Clear the target
ValidationTargetSequence = zeros(1,NumVelocities*TimeLength);
% Add a pulse for each velocity that is the same as the matched
% velocity
VelocityIndices = 1:NumVelocities;
for VelocityIndex = VelocityIndices(Velocities == MatchedVelocity)
% Get the insertion point for the data in the input sequence
InsertStart = 1 + (VelocityIndex - 1) * TimeLength;
InsertEnd = InsertStart + TimeLength - 1;
% Get a data set of the appropriate velocity with no noise
valParameters.NoiseLevel = 0;
Data = GetData(valParameters, MatchedVelocity, Time);
% Get the final channel
FinalChannel = Data(DataLines,:);
% Normalise it
FinalChannel = AgcSim(FinalChannel);
switch lower(valParameters.TargetOutput)
case 'pulse'
% Create a pulse at the 3dB points
TargetPulse = zeros(size(FinalChannel));
TargetPulse(FinalChannel >= (max(FinalChannel)/sqrt(2))) = 1;
% Apply it to the target sequence
ValidationTargetSequence(InsertStart:InsertEnd) = TargetPulse;
case 'ap'
% Target sequence is action potential with matched velocity
ValidationTargetSequence(InsertStart:InsertEnd) = FinalChannel;
otherwise
disp('Unknown output training target type.')
end;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Run the ANN training
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Normalise the data into the range 1 to -1 for each channel separately
TripolarNorm = AgcSim([TrainingInputSequence, ValidationInputSequence]);
%TripolarNorm = AgcSim(TrainingInputSequence);
TargetSequence = [TrainingTargetSequence, ValidationTargetSequence];
%TargetSequence = TrainingTargetSequence;
% Assad's ANN training setup
p = con2seq(TripolarNorm); % Cell array of input vectors
t = con2seq(TargetSequence); % Cell array of target vectors
d = cell(1, numel(Parameters.AnnLength));
for i = 1:numel(Parameters.AnnLength)
d{i} = 0:Parameters.AnnLength(i) - 1; % Delay vector for ith layer
end
net = distdelaynet(d, Parameters.HiddenLayerSize, Parameters.TrainingMethod);
net.layers{1:end-1}.transferFcn = Parameters.ActivationFcn1;
net.layers{end}.transferFcn = Parameters.ActivationFcn2;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Levenberg-Marquardt backpropagation default parameters
% net.trainParam.epochs = 1000; % Maximum number of epochs to train
% net.trainParam.goal = 0; % Performance goal
% net.trainParam.max_fail = 6; % Maximum validation failures
% net.trainParam.min_grad = 1e-7; % Minimum performance gradient
% net.trainParam.mu = 0.001; % Initial mu
net.trainParam.mu_dec = 0.1; % mu decrease factor
% net.trainParam.mu_inc = 10; % mu increase factor
% net.trainParam.mu_max = 1e10; % Maximum mu
% net.trainParam.show = 25; % Epochs between displays (NaN for no displays)
% net.trainParam.showCommandLine = 0; % Generate command-line output
% net.trainParam.showWindow = 1; % Show training GUI
% net.trainParam.time = Inf; % Maximum time to train in seconds
% net.trainParam.mem_reduc = 1; % Reduce memory and speed to calculate the Jacobian jX
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
net.trainParam.max_fail = 5;
net.divideFcn = 'divideind';
net.divideParam.trainInd = 1:size(TrainingInputSequence,2);
net.divideParam.valInd = (1:size(ValidationTargetSequence,2)) + ...
size(TrainingInputSequence,2);
% These are Assad's training criteria so they have been kept
net.trainParam.epochs = Parameters.MaxIterations;
net.trainParam.goal = 1e-6;
%net.divideFcn = 'dividetrain'; % Allocate all data for training
%net.trainParam.lr = 0.00005; % trainlm does not have a learning rate?
% net.outputConnect = [1 0];
% net.biasConnect = [0; 0];
% net.layers{1}.transferFcn = 'purelin';
% Do the ANN training
net = train(net,p,t);
% Store the ANN in an array
ANN(AnnIndex).net = net;
end