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33 lines
1.1 KiB
Matlab
33 lines
1.1 KiB
Matlab
function plot_weights()
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%Test encoding and decoding of simple, monomodal, gaussian probability distributions
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inputs=[-180:1:180];
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centres=[-180:20:180];
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%define weights, to produce a 1d basis function network, where nodes have gaussian RFs.
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W=[];
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for c=centres
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W=[W;code(c,inputs,10,0,1)];
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end
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figure(1),clf, plot(W','LineWidth',2)
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set(gca,'XTick',[1:90:360],'XTickLabel',[-180:90:179],'FontSize',14);
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axis([1,360,0,0.041])
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set(gcf,'PaperSize',[12 5],'PaperPosition',[0 0.25 12 4.5],'PaperOrientation','Portrait');
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print(gcf, '-dpdf', 'weights_noprior.pdf');
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%define weights, to produce a 1d basis function network, where nodes have gaussian RFs. Strength of RFs is modulated by prior distribution.
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priorMean=0;
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priorStd=60;
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priorDist=code(priorMean,inputs,priorStd);
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W=[];
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for c=centres
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W=[W;code(c,inputs,10,0,1).*priorDist];
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end
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figure(2), clf, plot(W','LineWidth',2)
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set(gca,'XTick',[1:90:360],'XTickLabel',[-180:90:179],'FontSize',14);
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axis([1,360,0,0.041])
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set(gcf,'PaperSize',[12 5],'PaperPosition',[0 0.25 12 4.5],'PaperOrientation','Portrait');
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print(gcf, '-dpdf', 'weights_prior.pdf');
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