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TimeAsMemory_v2.m~
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function [intervals,representations] = TimeAsMemory_v2(inputType, outputType, fixedLocation, ...
memorynoise, networknoise,showcolumnsgraph,...
showMainGraphs,trackdevelopment,showCorrelations, ...
showAttentionEffects)
% example of a neural network implementation of Embodied Memory model of
% time perception.
%
% The basic idea is in a mature system the time interval since an event
% is derived from of how blurry (some aspect of) the memory for
% the perceptual event has become. An immature system calibrates this
% mechanism using the predictable and repeatable signals from physical
% movements. These obey Fitt's law which is a direct analogue of the Scalar
% Property in interval timing.
% In this version, a network is 1st trained to predict the length of an
% interval from 1 distribution. Input is distribution of activations along
% a 1d vector and output is a log-scale counter (aka ATOM representation)
%
% Caspar Addyman 2010
% version 01 - 26 Sep 2010
% version 02 - 16 Dec 2010
% version 03 - 23 Jan 2011 - calibration version
% version 04 - 07 Apr 2012 - version for NCPW
close all;
%input curve types
ANALYTIC_GAUSSIAN = 0;
FADING_GAUSSIAN = 1;
SQSH_SPRD = 2;
%output curve types
LINEAR_OUTPUT = 1;
LOGLINEAR_OUTPUT = 0;
% Default settings
if nargin < 1, inputType = SQSH_SPRD; end %what shape is input function
if nargin < 2, outputType = LOGLINEAR_OUTPUT; end %what shape is input function
if nargin < 3, fixedLocation = true; end %is percept centred on input field?
if nargin < 4, memorynoise = false; end %is memory representation noisy
if nargin < 5, networknoise = true; end %internal network noise
if nargin < 6, showcolumnsgraph = true; end %show graph of co
if nargin < 7, showMainGraphs = true; end %show graph of main results
if nargin < 8, trackdevelopment = true; end %make note of weights at intervvals during learning
if nargin < 9, showCorrelations = false; end %plot graph of correlation between modalities over learning
if nargin < 10, showAttentionEffects = true; end %test the same for
if nargin < 11, showProspectiveAttentionEffects = true; end
includemodalitytwo = false;
modalitycount = 1 + includemodalitytwo;
nBabies = 20;
NEpochs = 10;
NTrainingItems = 10000;
LearningRate = 0.01;
momentum = 0.005;
OutputLoopSize = 10; %used for finding mean & std of network outputs
minInterval = 0.1; %seconds
maxInterval = 120; %seconds
numIntervals = 60;
%network architecture
inputWidth = 43; %how wide is input vector?
nHidNodes = 10;
outputWidth = 10; %how wide is output vector
%Analytic Gaussian params
memoryWidthUnit = 5;
memoryTimeUnit = 45;
memoryAmplitude = 10;
%Fading Gaussian params
% spread_factor = 0.0045;
% leakage_factor = 0.0105;
% self_excitation = 0.001;
spread_factor = 0.1;
leakage_factor = 0.7;
self_excitation = 0.001;
if networknoise
networknoiserate = 0.06;
else
networknoiserate = 0;
end
if memorynoise
%two types
%fixed amounts of noise added to all input nodes
memorynoise_fixed_mean = 0.05;
memorynoise_fixed_sd = 0.02;
%relative noise, sd is proportional to activation
memorynoise_proportional_sd = 0.03; %added to all input nodes
end
%%%%%%%%%%%%%%%%%%% INPUTS %%%%%%%%%%%%%%%%%%%%%%%%
% what type of input (& test) distribution do we have?
%
% we claim that memory is normal distribution that decays analogously to Fitt's law.
% so if width of stdev after K seconds is W, after 2K seconds it will be 2W
% therefore we need to specify a scale for both
%
% percept = setPercepts(inputType, minInterval,maxInterval,numIntervals, ...
% inputWidth,outputWidth,fixedLocation,...
% memoryWidthUnit,memoryTimeUnit,memoryAmplitude,...
% spread_factor,leakage_factor,self_excitation, ...
% lognormalIntervals,useBODYMapRepresentation,useBODYBinaryOut)
modality{1} = setPercepts(inputType,minInterval,maxInterval,numIntervals, ...
inputWidth, outputWidth,fixedLocation, ...
memoryWidthUnit,memoryTimeUnit,memoryAmplitude, ...
spread_factor,leakage_factor,self_excitation, ...
false, outputType, false);
modality{1} = populateInputsOutputs(modality{1});
%for the moment the second modality is very similar to first
modality{2} = setPercepts(inputType,minInterval,maxInterval,numIntervals, ...
inputWidth, outputWidth,fixedLocation, ...
memoryWidthUnit,memoryTimeUnit,memoryAmplitude, ...
spread_factor,leakage_factor,self_excitation, ...
false, outputType, false);
modality{2} = populateInputsOutputs(modality{2});
%%%%%%%%%%%%% EFFECT OF ATTENTION %%%%%%%%%%%%%%%%%%%%%%%%%%%
%another version of the same inputs and outputs but modulated by attention
% do this by applying multiplier to self-excitation
attentionMultiplier = 0.6;
attention{1} = setPercepts(inputType,minInterval,maxInterval,numIntervals, ...
inputWidth, outputWidth,fixedLocation, ...
memoryWidthUnit/ attentionMultiplier,memoryTimeUnit,memoryAmplitude, ...
spread_factor,leakage_factor,self_excitation * attentionMultiplier , ...
false, outputType, false);
attention{1} = populateInputsOutputs(attention{1});
attention{2} = setPercepts(inputType,minInterval,maxInterval,numIntervals, ...
inputWidth, outputWidth,fixedLocation, ...
memoryWidthUnit/attentionMultiplier,memoryTimeUnit,memoryAmplitude, ...
spread_factor,leakage_factor,self_excitation * attentionMultiplier, ...
false, outputType, false);
attention{2} = populateInputsOutputs(attention{1});
%%%%%%%%% SIMULATE N Babies %%%%%%%%%%%%%%%%%%%%%%
% so we can average performance
Baby = cell(nBabies,1);
for babycounter = 1:nBabies
for mod = 1:modalitycount
% sample random intervals in the approriate range (0.1 to 90s).
% perhaps these ought to have a poisson distribution but don't at the moment
% can just randomly sample from the rows of allPossibleIntervals
Baby{babycounter}.Modality{mod}.rows = randi([modality{mod}.numIntervals],NTrainingItems,1);
Baby{babycounter}.Modality{mod}.MemoryInputs = modality{mod}.MemoryCurves(Baby{babycounter}.Modality{mod}.rows, :);
Baby{babycounter}.Modality{mod}.ATOMOutputs = modality{mod}.ATOMOutputs(Baby{babycounter}.Modality{mod}.rows,:);
Baby{babycounter}.Modality{mod}.BODYOutputs = modality{mod}.BODYOutputs(Baby{babycounter}.Modality{mod}.rows,:);
if memorynoise
[R,C] = size(Baby{babycounter}.Modality{mod}.MemoryInputs);
Baby{babycounter}.Modality{mod}.NoisyInputs = Baby{babycounter}.Modality{mod}.MemoryInputs + ...
memorynoise_proportional_sd * Baby{babycounter}.Modality{mod}.MemoryInputs .* randn(R,C);
Baby{babycounter}.Modality{mod}.NoisyInputs = Baby{babycounter}.Modality{mod}.NoisyInputs + ...
memorynoise_fixed_mean + memorynoise_fixed_sd * rand(R,C);
else
Baby{babycounter}.Modality{mod}.NoisyInputs = Baby{babycounter}.Modality{mod}.MemoryInputs;
end
%%%%%%%%%%% the actual network training stage %%%%%%%%%%%%%%%
%%%%%%%%%%% using back prop.
if modality{1}.UseBODYMapRepresentation
[Baby{babycounter}.Modality{mod}.wt1 Baby{babycounter}.Modality{mod}.wt2] = backprop(Baby{babycounter}.Modality{mod}.NoisyInputs,Baby{babycounter}.Modality{mod}.BODYOutputs,nHidNodes,LearningRate,NEpochs,networknoiserate, momentum, 1,trackdevelopment);
else
[Baby{babycounter}.Modality{mod}.wt1 Baby{babycounter}.Modality{mod}.wt2] = backprop(Baby{babycounter}.Modality{mod}.NoisyInputs,Baby{babycounter}.Modality{mod}.ATOMOutputs,nHidNodes,LearningRate,NEpochs,networknoiserate, momentum, 1,trackdevelopment);
end
% %record the final weights
% wt1 = Baby{babycounter}.Modality{mod}.wt1{NEpochs};
% wt2 = Baby{babycounter}.Modality{mod}.wt2{NEpochs};
% save('trainedweights1.mat', 'wt1', 'wt2');
for devstage = 1:NEpochs
% now get set of representative outputs from our our trained network
% present each of the possible curves and see network prediction
% do this multiple times to get the prediction error (i.e. scalar property)
[R,C] = size(modality{mod}.MemoryCurves);
for k = 1:OutputLoopSize
if memorynoise
%add noise to memory decay curves
modality{mod}.NoisyInputs = modality{mod}.MemoryCurves + ...
memorynoise_proportional_sd * modality{mod}.MemoryCurves .* randn(R,C);
modality{mod}.NoisyInputs = modality{mod}.NoisyInputs + ...
memorynoise_fixed_mean + memorynoise_fixed_sd * rand(R,C);
%same for attention curves
attention{mod}.NoisyInputs = attention{mod}.MemoryCurves + ...
memorynoise_proportional_sd * attention{mod}.MemoryCurves .* randn(R,C);
attention{mod}.NoisyInputs = attention{mod}.NoisyInputs + ...
memorynoise_fixed_mean + memorynoise_fixed_sd * rand(R,C);
else
modality{mod}.NoisyInputs = modality{mod}.MemoryCurves;
attention{mod}.NoisyInputs = attention{mod}.MemoryCurves;
end
%record the network outputs
Baby{babycounter}.Modality{mod}.TrainedOutputs{devstage}{k} = backprop_out(modality{mod}.NoisyInputs,Baby{babycounter}.Modality{mod}.wt1{devstage},Baby{babycounter}.Modality{mod}.wt2{devstage},networknoiserate);
Baby{babycounter}.Modality{mod}.AttentionOutputs{devstage}{k} = backprop_out(attention{mod}.NoisyInputs,Baby{babycounter}.Modality{mod}.wt1{devstage},Baby{babycounter}.Modality{mod}.wt2{devstage},networknoiserate);
%convert network output representation into a time value
if modality{mod}.UseBODYMapRepresentation
Baby{babycounter}.Modality{mod}.TrainedOutTimes{devstage}(k,:) = BODYMapRepresentation(Baby{babycounter}.Modality{mod}.TrainedOutputs{devstage}{k},outputWidth,minInterval, maxInterval,true,modality{mod}.useBODYBinaryOut );
Baby{babycounter}.Modality{mod}.AttentionOutTimes{devstage}(k,:) = BODYMapRepresentation(Baby{babycounter}.Modality{mod}.AttentionOutputs{devstage}{k},outputWidth,minInterval, maxInterval,true,modality{mod}.useBODYBinaryOut );
else
Baby{babycounter}.Modality{mod}.TrainedOutTimes{devstage}(k,:) = ATOMrepresentation(Baby{babycounter}.Modality{mod}.TrainedOutputs{devstage}{k},outputWidth,minInterval, maxInterval,true);
Baby{babycounter}.Modality{mod}.AttentionOutTimes{devstage}(k,:) = ATOMrepresentation(Baby{babycounter}.Modality{mod}.AttentionOutputs{devstage}{k},outputWidth,minInterval, maxInterval,true);
end
end
%calculate mean and stdev for outputs
Baby{babycounter}.Modality{mod}.MeanOutput{devstage} = mean(Baby{babycounter}.Modality{mod}.TrainedOutTimes{devstage}, 1);
Baby{babycounter}.Modality{mod}.StdDevOutput{devstage} = std(Baby{babycounter}.Modality{mod}.TrainedOutTimes{devstage}, 1);
Baby{babycounter}.Modality{mod}.RelScalarErrors{devstage} = Baby{babycounter}.Modality{mod}.StdDevOutput{devstage} ./ Baby{babycounter}.Modality{mod}.MeanOutput{devstage};
Baby{babycounter}.Modality{mod}.AbsScalarErrors{devstage} = Baby{babycounter}.Modality{mod}.StdDevOutput{devstage} ./ modality{mod}.Intervals;
Baby{babycounter}.Modality{mod}.AttentionMeanOutput{devstage} = mean(Baby{babycounter}.Modality{mod}.AttentionOutTimes{devstage}, 1);
Baby{babycounter}.Modality{mod}.AttentionStdDevOutput{devstage} = std(Baby{babycounter}.Modality{mod}.AttentionOutTimes{devstage}, 1);
Baby{babycounter}.Modality{mod}.AttentionRelScalarErrors{devstage} = Baby{babycounter}.Modality{mod}.AttentionStdDevOutput{devstage} ./ Baby{babycounter}.Modality{mod}.AttentionMeanOutput{devstage};
Baby{babycounter}.Modality{mod}.AttentionAbsScalarErrors{devstage} = Baby{babycounter}.Modality{mod}.AttentionStdDevOutput{devstage} ./ modality{mod}.Intervals;
if showProspectiveAttentionEffects
for k = 1:OutputLoopSize
if memorynoise
%add noise to memory decay curves
modality{mod}.NoisyInputs = modality{mod}.MemoryCurves + ...
memorynoise_proportional_sd * modality{mod}.MemoryCurves .* randn(R,C)+ ...
memorynoise_fixed_mean + memorynoise_fixed_sd * rand(R,C);
%same for attention curves
attention{mod}.NoisyInputs = attention{mod}.MemoryCurves + ...
memorynoise_proportional_sd * attention{mod}.MemoryCurves .* randn(R,C) + ...
memorynoise_fixed_mean + memorynoise_fixed_sd * rand(R,C);
else
modality{mod}.NoisyInputs = modality{mod}.MemoryCurves;
attention{mod}.NoisyInputs = attention{mod}.MemoryCurves;
end
%To get the prospective time estimate outputs we let
%network cycle through memory curves until it's output
%reaches the target value then we see what input curve
%this actually represents.
tempOutputs = backprop_out(modality{mod}.NoisyInputs,Baby{babycounter}.Modality{mod}.wt1{devstage},Baby{babycounter}.Modality{mod}.wt2{devstage},networknoiserate);
tempAttentionOutputs = backprop_out(attention{mod}.NoisyInputs,Baby{babycounter}.Modality{mod}.wt1{devstage},Baby{babycounter}.Modality{mod}.wt2{devstage},networknoiserate);
%convert network outputs representation into time
%values
if modality{mod}.UseBODYMapRepresentation
tempEstimates = BODYMapRepresentation(tempOutputs,outputWidth,minInterval, maxInterval,true,modality{mod}.useBODYBinaryOut );
tempAttentionEstimates = BODYMapRepresentation(tempAttentionOutputs,outputWidth,minInterval, maxInterval,true,modality{mod}.useBODYBinaryOut );
else
tempEstimates = ATOMrepresentation(tempOutputs,outputWidth,minInterval, maxInterval,true);
tempAttentionEstimates = ATOMrepresentation(tempAttentionOutputs,outputWidth,minInterval, maxInterval,true);
end
for thisInterval = 1:modality{1}.numIntervals
targetTime = modality{1}.Intervals(thisInterval);
%first assign a maximum in case we don't reach it.
Baby{babycounter}.Modality{mod}.ProspectiveOutputs{devstage}(k,thisInterval) = modality{1}.Intervals(modality{1}.numIntervals);
for j=1:modality{1}.numIntervals
if tempEstimates(j) >= targetTime
Baby{babycounter}.Modality{mod}.ProspectiveOutputs{devstage}(k,thisInterval) = modality{1}.Intervals(j);
break;
end
end
end
%now do same again for the attention estimates
for thisInterval = 1:modality{1}.numIntervals
targetTime = modality{1}.Intervals(thisInterval);
%first assign a maximum in case we don't reach it.
Baby{babycounter}.Modality{mod}.ProspectiveAttentionOutputs{devstage}(k,thisInterval) = modality{1}.Intervals(modality{1}.numIntervals);
for j=1:modality{1}.numIntervals
if tempAttentionEstimates(j) >= targetTime
Baby{babycounter}.Modality{mod}.ProspectiveAttentionOutputs{devstage}(k,thisInterval) = modality{1}.Intervals(j);
break;
end
end
end
end
%calculate mean and stdev for outputs
Baby{babycounter}.Modality{mod}.ProspectiveMeanOutput{devstage} = mean(Baby{babycounter}.Modality{mod}.ProspectiveOutputs{devstage}, 1);
Baby{babycounter}.Modality{mod}.ProspectiveStdDevOutput{devstage} = std(Baby{babycounter}.Modality{mod}.ProspectiveOutputs{devstage}, 1);
Baby{babycounter}.Modality{mod}.ProspectiveRelScalarErrors{devstage} = Baby{babycounter}.Modality{mod}.ProspectiveStdDevOutput{devstage} ./ Baby{babycounter}.Modality{mod}.ProspectiveMeanOutput{devstage};
Baby{babycounter}.Modality{mod}.ProspectiveAbsScalarErrors{devstage} = Baby{babycounter}.Modality{mod}.ProspectiveStdDevOutput{devstage} ./ modality{mod}.Intervals;
Baby{babycounter}.Modality{mod}.ProspectiveAttentionMeanOutput{devstage} = mean(Baby{babycounter}.Modality{mod}.ProspectiveAttentionOutputs{devstage}, 1);
Baby{babycounter}.Modality{mod}.ProspectiveAttentionStdDevOutput{devstage} = std(Baby{babycounter}.Modality{mod}.ProspectiveAttentionOutputs{devstage} - ones(OutputLoopSize,1) * modality{mod}.Intervals, 1);
Baby{babycounter}.Modality{mod}.ProspectiveAttentionRelScalarErrors{devstage} = Baby{babycounter}.Modality{mod}.ProspectiveAttentionStdDevOutput{devstage} ./ Baby{babycounter}.Modality{mod}.ProspectiveAttentionMeanOutput{devstage};
Baby{babycounter}.Modality{mod}.ProspectiveAttentionAbsScalarErrors{devstage} = Baby{babycounter}.Modality{mod}.ProspectiveAttentionStdDevOutput{devstage} ./ modality{mod}.Intervals;
end
end
end
end
%%%%%%%%%% DISPLAY THE RESULTS %%%%%%%%%%%%%%%%%%%%%%%%%
%
% first get the average baby for each time and each stage of development
% there might be a more elegant way of doing this
% but this way i can understand what i am doing!
for babycounter = 1:nBabies
for ph = 1:modalitycount
for devstage = 1:NEpochs
AllOutTimes{ph}{devstage}(babycounter,:) = Baby{babycounter}.Modality{ph}.TrainedOutTimes{devstage}(1,:);
AllMeanTimes{ph}{devstage}(babycounter,:) = Baby{babycounter}.Modality{ph}.MeanOutput{devstage};
AllMeanErrors{ph}{devstage}(babycounter,:) = Baby{babycounter}.Modality{ph}.StdDevOutput{devstage};
AllRelErrors{ph}{devstage}(babycounter,:) = Baby{babycounter}.Modality{ph}.RelScalarErrors{devstage};
AllAbsErrors{ph}{devstage}(babycounter,:) = Baby{babycounter}.Modality{ph}.AbsScalarErrors{devstage};
%same for attention modulated values
AttnOutTimes{ph}{devstage}(babycounter,:) = Baby{babycounter}.Modality{ph}.AttentionOutTimes{devstage}(1,:);
AttnMeanTimes{ph}{devstage}(babycounter,:) = Baby{babycounter}.Modality{ph}.AttentionMeanOutput{devstage};
AttnMeanErrors{ph}{devstage}(babycounter,:) = Baby{babycounter}.Modality{ph}.AttentionStdDevOutput{devstage};
AttnRelErrors{ph}{devstage}(babycounter,:) = Baby{babycounter}.Modality{ph}.AttentionRelScalarErrors{devstage};
AttnAbsErrors{ph}{devstage}(babycounter,:) = Baby{babycounter}.Modality{ph}.AttentionAbsScalarErrors{devstage};
end
end
end
for devstage = 1:NEpochs
GlobalOutTimes{devstage} = mean(AllOutTimes{1}{devstage},1);
GlobalMeanTimes{devstage} = mean(AllMeanTimes{1}{devstage},1);
GlobalMeanErrors{devstage} = mean(AllMeanErrors{1}{devstage},1);
GlobalRelErrors{devstage} = mean(AllRelErrors{1}{devstage},1);
GlobalAbsErrors{devstage} = mean(AllAbsErrors{1}{devstage},1);
end
if showcolumnsgraph
%graph showing the inputs and outputs for representative set of time
%intervals
fig = figure(1);
%Set up figure dimensions for generating the animation
subplot(3,1,ph);
axis([0 modality{1}.inputWidth + 1 0 1.1]);
bar(1:modality{1}.inputWidth, modality{1}.MemoryCurves(1,:));
xlim([0 modality{1}.inputWidth + 1]);
ylim([0 2.2]);
xlabel('Input Columns');
ylabel('Activation');
title(['Input modality {' num2str(1) '}' ]);
%The output representation from the network
subplot(3,1,2)
axis([0 outputWidth + 1 0 1.1]);
if modality{2}.UseBODYMapRepresentation
bar(1:outputWidth, modality{1}.BODYOutputs(1,:));
else
bar(1:outputWidth, modality{1}.ATOMOutputs(1,:));
end
xlim([0 outputWidth + 1]);
ylim([0 1.1]);
xlabel('Output Columns');
ylabel('Activation');
title('Expected out');
%The output representation from the network
subplot(3,1,1)
axis([0 outputWidth + 1 0 1.1]);
bar(1:outputWidth, Baby{babycounter}.Modality{ph}.TrainedOutputs{NEpochs}{1}(1,:));
xlim([0 outputWidth + 1]);
ylim([0 1.1]);
xlabel('Output Columns');
ylabel('Activation');
title('Network out');
% Get figure size
pos = get(gcf, 'Position');
width = pos(3); height = pos(4);
% Preallocate data (for storing frame data)
mov = zeros(height, width, 1, length(T), 'uint8');
babycounter = nBabies; % just show last run
for ph = 1:modalitycount
disp(['Modality ' num2str(ph)]);
pause;
%First graph things after learning first modality
for t = 1:modality{ph}.numIntervals
set(fig, 'Name', ['Phase' num2str(ph) ' - Input Time t= ' num2str(t)]);
%The guassian distributions input on this phase
subplot(3,1,ph);
axis([0 modality{ph}.inputWidth + 1 0 1.1]);
bar(1:modality{ph}.inputWidth, modality{ph}.MemoryCurves(t,:));
xlim([0 modality{ph}.inputWidth + 1]);
ylim([0 2.2]);
xlabel('Input Columns');
ylabel('Activation');
title(['Input modality {' num2str(ph) '}' ]);
%The output representation from the network
subplot(3,1,2)
axis([0 outputWidth + 1 0 1.1]);
if modality{2}.UseBODYMapRepresentation
bar(1:outputWidth, modality{1}.BODYOutputs(t,:));
else
bar(1:outputWidth, modality{1}.ATOMOutputs(t,:));
end
xlim([0 outputWidth + 1]);
ylim([0 1.1]);
xlabel('Output Columns');
ylabel('Activation');
title('Expected out');
%The output representation from the network
subplot(3,1,1)
axis([0 outputWidth + 1 0 1.1]);
bar(1:outputWidth, Baby{babycounter}.Modality{ph}.TrainedOutputs{NEpochs}{1}(t,:));
xlim([0 outputWidth + 1]);
ylim([0 1.1]);
xlabel('Output Columns');
ylabel('Activation');
title('Network out');
% Get frame as an image
f = getframe(gcf);
% Create a colormap for the first frame. For the rest of the frames,
% use the same colormap
if id == 1
[mov(:,:,1,id), map] = rgb2ind(f.cdata, 256, 'nodither');
else
mov(:,:,1,id) = rgb2ind(f.cdata, map, 'nodither');
end
pause(0.2);
end
end
end
if showMainGraphs
%plot final performance with error bars.
figure(2);
for ph= 1:modalitycount
subplot(2,2,(ph-1)*2 +1);
hold on;
axis([0 maxInterval 0 maxInterval * 1.2]);
title(['Modality ' num2str(ph) ' predictions and errors'] );
xlabel('Time Interval /seconds');
ylabel('Prediction /seconds');
xlim([0 maxInterval]);
ylim([0 maxInterval]);
plot(modality{ph}.Intervals, modality{ph}.Intervals);
errorbar(modality{ph}.Intervals, Baby{babycounter}.Modality{ph}.MeanOutput{NEpochs}, 0.5* Baby{babycounter}.Modality{mod}.StdDevOutput{NEpochs});
hold off;
subplot(2,2,(ph-1)*2 +2);
axis([0 maxInterval 0 1.2]);
xlabel('Time Interval /seconds');
ylabel('Prediction /seconds');
xlim([0 maxInterval]);
ylim([0 1.2]);
hold on;
plot(modality{ph}.Intervals, Baby{babycounter}.Modality{ph}.RelScalarErrors{NEpochs}, ':+r');
plot(modality{ph}.Intervals, Baby{babycounter}.Modality{ph}.AbsScalarErrors{NEpochs}, ':*g');
title('Scaled output errors');
hold off;
end
%similar to figure 2 but showing performance over development
if trackdevelopment %check network performance after each Epoch
figure(3);
for ph = 1:modalitycount
subplot(2,2,(ph-1)*2 +1);
axis([0 maxInterval 0 maxInterval * 1.2]);
title(['Modality ' num2str(ph)]);
xlabel('Time Interval /seconds');
ylabel('Prediction /seconds');
xlim([0 maxInterval]);
ylim([0 maxInterval]);
hold on;
plot(modality{ph}.Intervals, modality{ph}.Intervals);
for devstage = 1:NEpochs
plot(modality{ph}.Intervals, Baby{babycounter}.Modality{ph}.MeanOutput{devstage});
end
hold off;
subplot(2,2,(ph-1)*2+2);
axis([0 maxInterval 0 1.2]);
xlabel('Time Interval /seconds');
ylabel('Prediction /seconds');
xlim([0 maxInterval]);
ylim([0 1.2]);
title('Average output Errors');
hold on;
for devstage = 1:NEpochs
plot(modality{ph}.Intervals, Baby{babycounter}.Modality{ph}.AbsScalarErrors{devstage}, ':+g');
end
hold off;
end
end
%results showing subplot 1,1 as a single figure for the paper
figure(5);
hold on;
title('Predictions over development');
axis([0 maxInterval 0 maxInterval * 1.2]);
xlabel('Time Interval / seconds');
ylabel('Prediction / seconds');
xlim([0 maxInterval]);
ylim([0 maxInterval]);
plot(modality{ph}.Intervals, modality{ph}.Intervals);
for devstage = 1:NEpochs
plot(modality{ph}.Intervals, GlobalMeanTimes{devstage});
end
hold off;
%results for modality as single figure for the paper
figure(6);
hold on;
title(['Global mean outputs and errors']);
axis([0 maxInterval 0 maxInterval * 1.2]);
xlabel('Time Interval /seconds');
ylabel('Prediction /seconds');
xlim([0 maxInterval]);
ylim([0 maxInterval]);
line(modality{ph}.Intervals, modality{ph}.Intervals);
errorbar(modality{ph}.Intervals, GlobalMeanTimes{NEpochs}, GlobalMeanErrors{NEpochs});
%errorbar(modality{ph}.Intervals, Baby{babycounter}.Modality{ph}.MeanOutput{NEpochs}, Baby{babycounter}.Modality{mod}.StdDevOutput{NEpochs});
ax1 = gca;
xlimits = get(ax1,'XLim');
ylimits = get(ax1,'YLim');
xinc = (xlimits(2)-xlimits(1))/5;
yinc = (ylimits(2)-ylimits(1))/5;
set(ax1,'XTick',[xlimits(1):xinc:xlimits(2)],...
'YTick',[ylimits(1):yinc:ylimits(2)])
ax2 = axes('Position',get(ax1,'Position'), ...
'XAxisLocation','bottom',...
'YAxisLocation','right',...
'Color','none',...
'XColor','w','YColor','k');
set(ax2,'XTick',[xlimits(1):xinc:xlimits(2)],...
'YTick',[0:0.2:1])
ylabel(ax2, 'Error as proportion of interval');
% set(get(ax2,'Ylabel'),'String','Fast Decay')
xlim(ax2, [0 maxInterval]);
ylim(ax2, [0 1]);
line(modality{ph}.Intervals, GlobalRelErrors{NEpochs}, 'Color', 'r', 'Parent', ax2);
% line(modality{ph}.Intervals, Baby{babycounter}.Modality{ph}.RelScalarErrors{NEpochs}, 'Color', 'r', 'Parent', ax2);
title('');
hold off;
end
%%% plotcorrelations
if showCorrelations
if modalitycount == 2
for n=1:NEpochs
rhomatrix = corrcoef(Baby{babycounter}.Modality{mod}.TrainedOutTimes{n}(1,:)',Baby{babycounter}.Modality{2}.TrainedOutTimes{n}(1,:)');
rho(n) = rhomatrix(1,2);
end
figure(4);
TrainingSteps = NTrainingItems:NTrainingItems:NEpochs*NTrainingItems;
plot(TrainingSteps, rho);
xlabel('Training Items');
ylabel('Correlation');
title('Correlation between modalities over development');
end
end
%%% plot AttentionEffect
if showAttentionEffects
figure(7);
for ph= 1:modalitycount
subplot(modalitycount,1,ph);
hold on;
axis([0 maxInterval 0 maxInterval * 1.2]);
xlabel('Time Interval /seconds');
ylabel('Prediction /seconds');
xlim([0 maxInterval]);
ylim([0 maxInterval]);
plot(modality{ph}.Intervals, modality{ph}.Intervals);
errorbar(modality{ph}.Intervals, Baby{babycounter}.Modality{ph}.MeanOutput{NEpochs}, 0.5* Baby{babycounter}.Modality{mod}.StdDevOutput{NEpochs},'-+b');
errorbar(modality{ph}.Intervals, Baby{babycounter}.Modality{ph}.AttentionMeanOutput{NEpochs}, 0.5* Baby{babycounter}.Modality{mod}.AttentionStdDevOutput{NEpochs},'-*g');
title('Effect of attention on retrospective time estimates');
hold off;
end
end
%%% plot AttentionEffect
if showProspectiveAttentionEffects
figure(8);
for ph= 1:modalitycount
subplot(modalitycount,1,ph);
hold on;
axis([0 maxInterval 0 maxInterval * 1.2]);
xlabel('Time Interval /seconds');
ylabel('Prediction /seconds');
xlim([0 maxInterval]);
ylim([0 maxInterval]);
plot(modality{ph}.Intervals, modality{ph}.Intervals);
errorbar(modality{ph}.Intervals, Baby{babycounter}.Modality{ph}.ProspectiveMeanOutput{NEpochs}, 0.5* Baby{babycounter}.Modality{mod}.ProspectiveStdDevOutput{NEpochs},'-+b');
errorbar(modality{ph}.Intervals, Baby{babycounter}.Modality{ph}.ProspectiveAttentionMeanOutput{NEpochs}, 0.5* Baby{babycounter}.Modality{mod}.ProspectiveAttentionStdDevOutput{NEpochs},'-*r');
title('Effect of attention on prospective time estimates');
hold off;
end
end