585 lines
23 KiB
Mathematica
585 lines
23 KiB
Mathematica
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filename;
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dmS = DataManipulator('');
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dmS.loadSourceCSV(filename);
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dmT = DataManipulator('');
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dmT.loadTargetCSV(filename);
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crSource = [];
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crTarget = [];
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trainTime = [];
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testTime = [];
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KlLossEvolution = [];
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classificationLoss = [];
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nodeEvolution = [];
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discriminativeLoss = [];
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generativeLossTarget = [];
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agmmTargetGenSize = [];
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agmmSourceDiscSize = [];
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nodeEvolutionTarget = [];
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nodeEvolutionSource = [];
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gmmTargetBatch = [];
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gmmSourceBatch = [];
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nn = NeuralNetwork([dmS.nFeatures 1 dmS.nClasses]);
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ae = DenoisingAutoEncoder([nn.layers(1) nn.layers(2) nn.layers(1)]);
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% I am building the greedyLayerBias
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x = dmS.getX(1);
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ae.greddyLayerWiseTrain(x(1, :), 1, 0.1);
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% I am building the greedyLayerBias
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agmmSourceDisc = AGMM();
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agmmTargetGen = AGMM();
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sourceSize = size(dmS.data, 1);
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targetSize = size(dmT.data, 1);
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sourceIndex = 0;
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targetIndex = 0;
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i = 0;
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originalLearningRate = ae.learningRate;
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epochs = 1;
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while (sourceIndex + targetIndex) < (sourceSize + targetSize)
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i = i + 1;
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Xs = [];
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ys = [];
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Xt = [];
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yt = [];
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batchCount = 0;
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while batchCount < 1000 && (sourceIndex + targetIndex) <= (sourceSize + targetSize)
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ratio = (sourceSize - sourceIndex) / (sourceSize + targetSize - sourceIndex - targetIndex);
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if (rand(1) <= ratio && sourceIndex < sourceSize) || (targetIndex >= targetSize && sourceIndex < sourceSize)
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sourceIndex = sourceIndex + 1;
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Xs = [Xs; dmS.getX(sourceIndex)];
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ys = [ys; dmS.getY(sourceIndex)];
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elseif targetIndex < targetSize
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targetIndex = targetIndex + 1;
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Xt = [Xt; dmT.getX(targetIndex)];
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yt = [yt; dmT.getY(targetIndex)];
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end
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batchCount = batchCount + 1;
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end
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%% workaround
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if size(Xs, 1) == 0
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Xs = [Xs, dmS.getX(sourceSize)];
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ys = [ys, dmS.getY(sourceSize)];
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end
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if size(Xt, 1) == 0
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Xt = [Xt, dmT.getX(targetSize)];
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yt = [yt, dmT.getY(targetSize)];
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end
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%% Evaluation ~ Test Target
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tic
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nn.test(Xt, yt);
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crTarget(end + 1) = nn.classificationRate;
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classificationLoss(end + 1) = nn.lossValue;
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testTime(end + 1) = toc;
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nn.test(Xs(max(Xs, [], 2) ~= 0, :), ys(max(Xs, [], 2) ~= 0, :));
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crSource(end + 1) = nn.classificationRate;
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discriminativeLoss(end + 1) = nn.lossValue;
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ae.test(Xt);
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generativeLossTarget(end + 1) = ae.lossValue;
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if i > 1
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nodeEvolutionTarget(end + 1) = nodeEvolutionTarget(i - 1);
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nodeEvolutionSource(end + 1) = nodeEvolutionSource(i - 1);
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else
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nodeEvolutionTarget(end + 1) = 0;
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nodeEvolutionSource(end + 1) = 0;
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end
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tic
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for epoch = 1 : epochs
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%% Discriminative phase on Source
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nn.setAgmm(agmmSourceDisc);
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for j = 1 : size(Xs, 1)
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x = Xs(j, :);
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y = ys(j, :);
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if max(y) == 0
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continue
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end
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lastHiddenLayerNo = numel(nn.layers) - 1;
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nn.forwardpass(x);
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if epoch == 1
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agmmSourceDiscSize(end + 1) = nn.runAgmm(x, y).M();
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nn.widthAdaptationStepwise(lastHiddenLayerNo, y);
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else
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nn.nSamplesFeed = nn.nSamplesFeed - 1;
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nn.nSamplesLayer(lastHiddenLayerNo) = nn.nSamplesLayer(lastHiddenLayerNo) - 1;
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nn.widthAdaptationStepwise(lastHiddenLayerNo, y);
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nn.BIAS2{lastHiddenLayerNo}(end) = [];
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nn.VAR{lastHiddenLayerNo}(end) = [];
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end
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if nn.growable(lastHiddenLayerNo)
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nodeEvolutionSource(i) = nodeEvolutionSource(i) + nn.getAgmm().M();
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for numberOfGMMs = 1 : nn.getAgmm().M()
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nn.grow(lastHiddenLayerNo);
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ae.grow(lastHiddenLayerNo);
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end
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elseif nn.prunable{lastHiddenLayerNo}(1) ~= 0
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for k = size(nn.prunable{lastHiddenLayerNo}, 1) : -1 : 1
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nodeToPrune = nn.prunable{lastHiddenLayerNo}(k);
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ae.prune(lastHiddenLayerNo, nodeToPrune);
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nn.prune(lastHiddenLayerNo, nodeToPrune);
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nodeEvolutionSource(i) = nodeEvolutionSource(i) - 1;
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end
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end
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nn.train(x, y);
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end
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for j = 1 : numel(nn.layers)-2
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ae.weight{j} = nn.weight{j};
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ae.bias{j} = nn.bias{j};
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end
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agmmSourceDisc = nn.getAgmm();
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%% Generative phase on Target
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ae.setAgmm(agmmTargetGen);
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for j = 1 : size(Xt, 1)
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x = Xt(j, :);
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y = x;
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lastHiddenLayerNo = numel(nn.layers) - 1;
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ae.forwardpass(x);
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if epoch == 1
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agmmTargetGenSize(end + 1) = ae.runAgmm(x, y).M();
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ae.widthAdaptationStepwise(lastHiddenLayerNo, y);
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else
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ae.nSamplesFeed = ae.nSamplesFeed - 1;
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ae.nSamplesLayer(lastHiddenLayerNo) = ae.nSamplesLayer(lastHiddenLayerNo) - 1;
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ae.widthAdaptationStepwise(lastHiddenLayerNo, y);
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ae.BIAS2{lastHiddenLayerNo}(end) = [];
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ae.VAR{lastHiddenLayerNo}(end) = [];
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end
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if ae.growable(lastHiddenLayerNo)
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nodeEvolutionTarget(i) = nodeEvolutionTarget(i) + ae.getAgmm().M();
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for numberOfGMMs = 1 : ae.getAgmm.M()
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ae.grow(lastHiddenLayerNo);
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nn.grow(lastHiddenLayerNo);
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end
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elseif ae.prunable{lastHiddenLayerNo}(1) ~= 0
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for k = size(ae.prunable{lastHiddenLayerNo}, 1) : -1 : 1
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nodeToPrune = ae.prunable{lastHiddenLayerNo}(k);
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ae.prune(lastHiddenLayerNo, nodeToPrune);
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nn.prune(lastHiddenLayerNo, nodeToPrune);
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nodeEvolutionTarget(i) = nodeEvolutionTarget(i) - 1;
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end
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end
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ae.greddyLayerWiseTrain(x, 1, 0.1);
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end
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for j = 1 : numel(ae.layers)-2
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nn.weight{j} = ae.weight{j};
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nn.bias{j} = ae.bias{j};
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end
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agmmTargetGen = ae.getAgmm();
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% Kullback-Leibler Divergence
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try
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common = min(size(Xs,1), size(Xt,1));
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KlLossEvolution(end + 1) = ae.updateWeightsByKullbackLeibler(Xs(1:common,:), Xt(1:common,:));
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catch
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KlLossEvolution(end + 1) = 0;
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end
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for j = 1 : numel(ae.layers)-2
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nn.weight{j} = ae.weight{j};
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nn.bias{j} = ae.bias{j};
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end
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end
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if agmmSourceDisc.M() > 1
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agmmSourceDisc.deleteCluster();
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end
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if agmmTargetGen.M() > 1
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agmmTargetGen.deleteCluster();
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end
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trainTime(end + 1) = toc;
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gmmTargetBatch(end + 1) = agmmTargetGen.M();
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gmmSourceBatch(end + 1) = agmmSourceDisc.M();
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%% Print metrics
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nodeEvolution(i, :) = nn.layers(2 : end - 1);
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if i == 2 || mod(i, round((sourceSize + targetSize)/1000/10)) == 0
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fprintf('Minibatch: %d\n', i);
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fprintf('Total of samples: %d Source | %d Target\n', size(Xs,1), size(Xt,1));
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fprintf('Max Mean Min Now Accu Training time: %f %f %f %f %f\n', max(trainTime(1:i)), mean(trainTime(1:i)), min(trainTime(1:i)), trainTime(i), sum(trainTime(1:i)));
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fprintf('Max Mean Min Now Accu Testing time: %f %f %f %f %f\n', max(testTime(1:i)), mean(testTime(1:i)), min(testTime(1:i)), testTime(i), sum(testTime(1:i)));
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fprintf('Max Mean Min Now AGMM Source: %d %f %d %d\n', max(agmmSourceDiscSize), mean(agmmSourceDiscSize), min(agmmSourceDiscSize), agmmSourceDiscSize(end));
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fprintf('Max Mean Min Now AGMM Target: %d %f %d %d\n', max(agmmTargetGenSize), mean(agmmTargetGenSize), min(agmmTargetGenSize), agmmTargetGenSize(end));
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fprintf('Max Mean Min Now CR: %f%% %f%% %f%% %f%%\n', max(crTarget(2:i)) * 100., mean(crTarget(2:i)) * 100., min(crTarget(2:i)) * 100., crTarget(i) * 100.);
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fprintf('Max Mean Min Now Classification Loss: %f %f %f %f\n', max(classificationLoss(2:i)), mean(classificationLoss(2:i)), min(classificationLoss(2:i)), classificationLoss(i));
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fprintf('Max Mean Min Now KL: %f %f %f %f\n', max(KlLossEvolution(2:i)), mean(KlLossEvolution(2:i)), min(KlLossEvolution(2:i)), KlLossEvolution(i));
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fprintf('Max Mean Min Now Nodes: %d %f %d %d\n', max(nodeEvolution(2:i)), mean(nodeEvolution(2:i)), min(nodeEvolution(2:i)), nodeEvolution(i));
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fprintf('Network structure: %s (Discriminative) | %s (Generative)\n', num2str(nn.layers(:).'), num2str(ae.layers(:).'));
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fprintf('\n');
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end
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end
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fprintf('\n\n')
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fprintf('Source CR: %f\n', mean(crSource(2:end)))
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fprintf('Target CR: %f\n', mean(crTarget(2:end)))
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fprintf('Training time: %f\n', sum(trainTime))
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%% ---------------------------- Plotters ----------------------------------
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function plotTime(trainTime, testTime)
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figure('Name', 'Processing Time', 'NumberTitle', 'off');
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hold on
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ylim([0 max(max(trainTime), max(testTime)) * 1.1]);
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xlim([1 size(trainTime, 2)]);
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pTrain = plot(trainTime);
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pTest = plot(testTime);
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if max(trainTime) > 1
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text(find(trainTime == max(trainTime(trainTime > 1)), 1), max(trainTime(trainTime > 1)),...
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strcat('\leftarrow Max Train Time:', {' '}, string(max(trainTime(trainTime > 1)))),...
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'FontSize', 8,...
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'Color', 'black');
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text(find(trainTime == min(trainTime(trainTime > 1)), 1), min(trainTime(trainTime > 1)),...
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strcat('\leftarrow Min Train Time:', {' '}, string(min(trainTime(trainTime > 1)))),...
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'FontSize', 8,...
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'Color', 'black');
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end
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if max(testTime) > 1
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text(find(testTime == max(testTime(testTime > 1)), 1), max(testTime(testTime > 1)),...
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strcat('\leftarrow Max Test Time:', {' '}, string(max(testTime(testTime > 1)))),...
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'FontSize', 8,...
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'Color', 'black');
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text(find(testTime == min(testTime(testTime > 1)), 1), min(testTime(testTime > 1)),...
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strcat('\leftarrow Min Test Time:', {' '}, string(min(testTime(testTime > 1)))),...
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'FontSize', 8,...
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'Color', 'black');
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end
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legend([pTrain,...
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pTest], [strcat('Train Time Mean | Accumulative:', {' '}, string(mean(trainTime)), {' | '}, string(sum(trainTime))),...
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strcat('Test Time Mean | Accumulative:', {' '}, string(mean(testTime)), {' | '}, string(sum(testTime)))]);
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ylabel('Time in seconds');
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xlabel('Minibatches');
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hold off
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end
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function plotNodeEvolution(nodeEvolution)
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figure('Name', 'Node Evolution', 'NumberTitle', 'off');
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hold on
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ylim([0 max(nodeEvolution, [], 'all') * 1.1]);
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xlim([1 size(nodeEvolution, 1)]);
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plotArray = [];
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legendArray = [];
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for i = 1 : size(nodeEvolution, 2)
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p = plot(nodeEvolution(:, i));
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plotArray = [plotArray, p];
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legendArray = [legendArray, strcat('HL', {' '}, string(i), {' '}, 'mean:', {' '}, string(mean(nodeEvolution(nodeEvolution(:, i) > 0, i))))];
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text(find(nodeEvolution(:, i) == max(nodeEvolution(:, i)), 1), max(nodeEvolution(:, i)),...
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strcat('\leftarrow Max nodes HL ', {' '}, string(i), ':', {' '}, string(max(nodeEvolution(:, i)))),...
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'FontSize', 8,...
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'Color', 'black');
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text(find(nodeEvolution(:, i) == min(nodeEvolution(nodeEvolution(:, i) > 0, i)), 1), min(nodeEvolution(nodeEvolution(:, i) > 0, i)),...
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strcat('\leftarrow Min nodes HL ', {' '}, string(i), ':', {' '}, string(min(nodeEvolution(nodeEvolution(:, i) > 0, i)))),...
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'FontSize', 8,...
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'Color', 'black');
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end
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ylabel('Number of nodes');
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xlabel('Minibatches');
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legend(plotArray, legendArray);
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hold off
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end
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function plotAGMM(agmmSource, agmmTarget)
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figure('Name', 'Number of GMMs on AGMMs', 'NumberTitle', 'off');
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hold on
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ylim([0 max(max(agmmTarget), max(agmmSource)) * 1.1]);
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xlim([1 size(agmmSource, 2)]);
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pAgmmSource = plot(agmmSource);
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pAgmmTarget = plot(agmmTarget);
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if max(agmmSource) > 1
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text(find(agmmSource == max(agmmSource(agmmSource > 1)), 1), max(agmmSource(agmmSource > 1)),...
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strcat('\leftarrow Max GMMs Source Discriminative:', {' '}, string(max(agmmSource(agmmSource > 1)))),...
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'FontSize', 8,...
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'Color', 'black');
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text(find(agmmSource == min(agmmSource(agmmSource > 1)), 1), min(agmmSource(agmmSource > 1)),...
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strcat('\leftarrow Min GMMs Source Discriminative:', {' '}, string(min(agmmSource(agmmSource > 1)))),...
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||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
end
|
||
|
|
if max(agmmTarget) > 1
|
||
|
|
text(find(agmmTarget == max(agmmTarget(agmmTarget > 1)), 1), max(agmmTarget(agmmTarget > 1)),...
|
||
|
|
strcat('\leftarrow Max GMMs Target Generative:', {' '}, string(max(agmmTarget(agmmTarget > 1)))),...
|
||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
text(find(agmmTarget == min(agmmTarget(agmmTarget > 1)), 1), min(agmmTarget(agmmTarget > 1)),...
|
||
|
|
strcat('\leftarrow Min GMMs Target Generative:', {' '}, string(min(agmmTarget(agmmTarget > 1)))),...
|
||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
end
|
||
|
|
|
||
|
|
legend([pAgmmSource,...
|
||
|
|
pAgmmTarget], [strcat('AGMM Source Discriminative Mean:', {' '}, string(mean(agmmSource))),...
|
||
|
|
strcat('AGMM Target Generative Mean:', {' '}, string(mean(agmmTarget)))]);
|
||
|
|
|
||
|
|
|
||
|
|
ylabel('Number of GMMs');
|
||
|
|
xlabel('Samples');
|
||
|
|
|
||
|
|
hold off
|
||
|
|
end
|
||
|
|
|
||
|
|
function plotLosses(classificationLoss, discriminativeLoss, generativeTargetLoss, kullbackLeiblerLoss)
|
||
|
|
figure('Name', 'Losses', 'NumberTitle', 'off');
|
||
|
|
hold on
|
||
|
|
|
||
|
|
ylim([0 max(max(kullbackLeiblerLoss), max(max(max(classificationLoss), max(discriminativeLoss)), max(generativeTargetLoss))) * 1.1]);
|
||
|
|
xlim([1 size(classificationLoss, 1)]);
|
||
|
|
|
||
|
|
pClassificationLoss = plot(classificationLoss);
|
||
|
|
pDiscriminativeLoss = plot(discriminativeLoss);
|
||
|
|
pGenerativeTargetLoss = plot(generativeTargetLoss);
|
||
|
|
pKullbackLeiblerLoss = plot(kullbackLeiblerLoss);
|
||
|
|
|
||
|
|
text(find(classificationLoss == max(classificationLoss), 1), max(classificationLoss),...
|
||
|
|
strcat('\leftarrow Max Classification Loss:', {' '}, string(max(classificationLoss))),...
|
||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
text(find(classificationLoss == min(classificationLoss), 1), min(classificationLoss),...
|
||
|
|
strcat('\leftarrow Min Classification Loss:', {' '}, string(min(classificationLoss))),...
|
||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
|
||
|
|
text(find(discriminativeLoss == max(discriminativeLoss), 1), max(discriminativeLoss),...
|
||
|
|
strcat('\leftarrow Max Discriminative Loss:', {' '}, string(max(discriminativeLoss))),...
|
||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
text(find(discriminativeLoss == min(discriminativeLoss), 1), min(discriminativeLoss),...
|
||
|
|
strcat('\leftarrow Min Discriminative Loss:', {' '}, string(min(discriminativeLoss))),...
|
||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
|
||
|
|
text(find(generativeTargetLoss == max(generativeTargetLoss), 1), max(generativeTargetLoss),...
|
||
|
|
strcat('\leftarrow Max Generative Target Loss:', {' '}, string(max(generativeTargetLoss))),...
|
||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
text(find(generativeTargetLoss == min(generativeTargetLoss), 1), min(generativeTargetLoss),...
|
||
|
|
strcat('\leftarrow Min Generative Target Loss:', {' '}, string(min(generativeTargetLoss))),...
|
||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
|
||
|
|
text(find(kullbackLeiblerLoss == max(kullbackLeiblerLoss), 1), max(kullbackLeiblerLoss),...
|
||
|
|
strcat('\leftarrow Max KL Div Loss:', {' '}, string(max(kullbackLeiblerLoss))),...
|
||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
text(find(kullbackLeiblerLoss == min(kullbackLeiblerLoss), 1), min(kullbackLeiblerLoss),...
|
||
|
|
strcat('\leftarrow Min KL Div Loss:', {' '}, string(min(kullbackLeiblerLoss))),...
|
||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
|
||
|
|
ylabel('Loss Value');
|
||
|
|
xlabel('Minibatches');
|
||
|
|
|
||
|
|
legend([pClassificationLoss,...
|
||
|
|
pDiscriminativeLoss,...
|
||
|
|
pGenerativeTargetLoss,...
|
||
|
|
pKullbackLeiblerLoss], [strcat('Classification Loss Mean:', {' '}, string(mean(classificationLoss(2:end)))),...
|
||
|
|
strcat('Discriminative Loss Mean:', {' '}, string(mean(discriminativeLoss))),...
|
||
|
|
strcat('Generative Target Loss Mean:', {' '}, string(mean(generativeTargetLoss))),...
|
||
|
|
strcat('Kullback Leibler Divergence Loss Mean:', {' '}, string(mean(kullbackLeiblerLoss)))]);
|
||
|
|
|
||
|
|
hold off
|
||
|
|
end
|
||
|
|
|
||
|
|
function plotClassificationRate(source, target, nMinibatches)
|
||
|
|
figure('Name', 'Source and Target Classification Rates', 'NumberTitle', 'off');
|
||
|
|
hold on
|
||
|
|
ylim([0 max(max(source), max(target)) * 1.1]);
|
||
|
|
xlim([1 nMinibatches]);
|
||
|
|
|
||
|
|
niceBlue = [0 0.4470 0.7410];
|
||
|
|
niceYellow = [0.8500 0.3250 0.0980];
|
||
|
|
|
||
|
|
pSource = plot(source, 'Color', niceYellow, 'LineStyle', ':');
|
||
|
|
pTarget = plot(target, 'Color', niceBlue);
|
||
|
|
|
||
|
|
text(find(source == max(source), 1), max(source),...
|
||
|
|
strcat('\leftarrow Max Source:', {' '}, string(max(source))),...
|
||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
text(find(source == min(source), 1), min(source),...
|
||
|
|
strcat('\leftarrow Min Source:', {' '}, string(min(source))),...
|
||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
text(find(target == max(target), 1), max(target),...
|
||
|
|
strcat('\leftarrow Max Target:', {' '}, string(max(target))),...
|
||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
text(find(target == min(target), 1), min(target),...
|
||
|
|
strcat('\leftarrow Min Target:', {' '}, string(min(target))),...
|
||
|
|
'FontSize', 8,...
|
||
|
|
'Color', 'black');
|
||
|
|
|
||
|
|
ylabel('Classification Rate');
|
||
|
|
xlabel('Minibatches');
|
||
|
|
|
||
|
|
legend([pSource, pTarget], [strcat('Source Mean:', {' '}, string(mean(source(2:end)))),...
|
||
|
|
strcat('Target Mean:', {' '}, string(mean(target(2:end))))]);
|
||
|
|
|
||
|
|
hold off
|
||
|
|
|
||
|
|
end
|
||
|
|
|
||
|
|
function plotBIAS2andVAR(BIAS2, VAR)
|
||
|
|
sampleLayerCount = zeros(1, size(BIAS2, 2));
|
||
|
|
yAxisLim = 0;
|
||
|
|
bias2 = [];
|
||
|
|
var = [];
|
||
|
|
for i = 2 : size(BIAS2, 2)
|
||
|
|
sampleLayerCount(i) = sampleLayerCount(i - 1) + size(BIAS2{i}, 2);
|
||
|
|
for j = 1 : size(BIAS2{i}, 2)
|
||
|
|
bias2 = [bias2, BIAS2{i}(j)];
|
||
|
|
var = [var, VAR{i}(j)];
|
||
|
|
yAxisLim = max(yAxisLim, bias2(end) + var(end));
|
||
|
|
end
|
||
|
|
end
|
||
|
|
clear BIAS2 VAR
|
||
|
|
|
||
|
|
figure('Name', 'BIAS2, VAR, and NS', 'NumberTitle', 'off');
|
||
|
|
hold on
|
||
|
|
|
||
|
|
ylim([0 max(max(bias2), max(var)) * 1.1]);
|
||
|
|
xlim([1 size(bias2, 2)]);
|
||
|
|
|
||
|
|
p1 = plot(bias2);
|
||
|
|
p2 = plot(var);
|
||
|
|
p3 = plot(bias2 + var);
|
||
|
|
for j = 1: ceil(size(bias2, 2)/4) : size(bias2, 2)
|
||
|
|
if ~isnan(bias2(j))
|
||
|
|
text(j, bias2(j),...
|
||
|
|
strcat('\leftarrow', {' '}, 'BIAS2 =', {' '}, string(bias2(j))),...
|
||
|
|
'FontSize', 8);
|
||
|
|
end
|
||
|
|
end
|
||
|
|
text(size(bias2, 2), bias2(end), string(bias2(end)));
|
||
|
|
|
||
|
|
for j = 1: ceil(size(var, 2)/4) : size(var, 2)
|
||
|
|
if ~isnan(var(j))
|
||
|
|
text(j, var(j),...
|
||
|
|
strcat('\leftarrow', {' '}, 'VAR =', {' '}, string(var(j))),...
|
||
|
|
'FontSize', 8);
|
||
|
|
end
|
||
|
|
end
|
||
|
|
text(size(var, 2), var(end), string(var(end)));
|
||
|
|
|
||
|
|
for j = 1: ceil(size(var + bias2, 2)/4) : size(var + bias2, 2)
|
||
|
|
if ~isnan(var(j)) && ~isnan(bias2(j)) && ~isnan(var(j) + bias2(j))
|
||
|
|
text(j, var(j) + bias2(j),...
|
||
|
|
strcat('\leftarrow', {' '}, 'NS =', {' '}, string(var(j) + bias2(j))),...
|
||
|
|
'FontSize', 8);
|
||
|
|
end
|
||
|
|
end
|
||
|
|
text(size(var + bias2, 2), var(end) + bias2(end), string(var(end) + bias2(end)));
|
||
|
|
|
||
|
|
for i = 2 : size(sampleLayerCount, 2) - 1
|
||
|
|
line([sampleLayerCount(i), sampleLayerCount(i)], [-yAxisLim * 2 yAxisLim * 2],...
|
||
|
|
'LineStyle', ':',...
|
||
|
|
'Color', 'magenta');
|
||
|
|
end
|
||
|
|
|
||
|
|
ylabel('Value');
|
||
|
|
xlabel('Sample');
|
||
|
|
|
||
|
|
legend([p1, p2, p3], [strcat('BIAS2 Mean:', {' '}, string(mean(bias2(2:end)))),...
|
||
|
|
strcat('VAR Mean:', {' '}, string(mean(var(2:end)))),...
|
||
|
|
strcat('NS Mean:', {' '}, string(mean(var(2:end) + bias2(2:end))))]);
|
||
|
|
hold off
|
||
|
|
end
|
||
|
|
|
||
|
|
function plotBIAS2andVARGen(BIAS2, VAR)
|
||
|
|
sampleLayerCount = zeros(1, size(BIAS2, 2));
|
||
|
|
yAxisLim = 0;
|
||
|
|
bias2 = [];
|
||
|
|
var = [];
|
||
|
|
for i = 2 : size(BIAS2, 2)
|
||
|
|
sampleLayerCount(i) = sampleLayerCount(i - 1) + size(BIAS2{i}, 2);
|
||
|
|
for j = 1 : size(BIAS2{i}, 2)
|
||
|
|
bias2 = [bias2, BIAS2{i}(j)];
|
||
|
|
var = [var, VAR{i}(j)];
|
||
|
|
yAxisLim = max(yAxisLim, bias2(end) + var(end));
|
||
|
|
end
|
||
|
|
end
|
||
|
|
clear BIAS2 VAR
|
||
|
|
|
||
|
|
figure('Name', 'BIAS2, VAR, and NS Generative', 'NumberTitle', 'off');
|
||
|
|
hold on
|
||
|
|
|
||
|
|
ylim([0 max(max(bias2), max(var)) * 1.1]);
|
||
|
|
xlim([1 size(bias2, 2)]);
|
||
|
|
|
||
|
|
p1 = plot(bias2);
|
||
|
|
p2 = plot(var);
|
||
|
|
p3 = plot(bias2 + var);
|
||
|
|
for j = 1: ceil(size(bias2, 2)/4) : size(bias2, 2)
|
||
|
|
if ~isnan(bias2(j))
|
||
|
|
text(j, bias2(j),...
|
||
|
|
strcat('\leftarrow', {' '}, 'BIAS2 =', {' '}, string(bias2(j))),...
|
||
|
|
'FontSize', 8);
|
||
|
|
end
|
||
|
|
end
|
||
|
|
text(size(bias2, 2), bias2(end), string(bias2(end)));
|
||
|
|
|
||
|
|
for j = 1: ceil(size(var, 2)/4) : size(var, 2)
|
||
|
|
if ~isnan(var(j))
|
||
|
|
text(j, var(j),...
|
||
|
|
strcat('\leftarrow', {' '}, 'VAR =', {' '}, string(var(j))),...
|
||
|
|
'FontSize', 8);
|
||
|
|
end
|
||
|
|
end
|
||
|
|
text(size(var, 2), var(end), string(var(end)));
|
||
|
|
|
||
|
|
for j = 1: ceil(size(var + bias2, 2)/4) : size(var + bias2, 2)
|
||
|
|
if ~isnan(var(j)) && ~isnan(bias2(j)) && ~isnan(var(j) + bias2(j))
|
||
|
|
text(j, var(j) + bias2(j),...
|
||
|
|
strcat('\leftarrow', {' '}, 'NS =', {' '}, string(var(j) + bias2(j))),...
|
||
|
|
'FontSize', 8);
|
||
|
|
end
|
||
|
|
end
|
||
|
|
text(size(var + bias2, 2), var(end) + bias2(end), string(var(end) + bias2(end)));
|
||
|
|
|
||
|
|
for i = 2 : size(sampleLayerCount, 2) - 1
|
||
|
|
line([sampleLayerCount(i), sampleLayerCount(i)], [-yAxisLim * 2 yAxisLim * 2],...
|
||
|
|
'LineStyle', ':',...
|
||
|
|
'Color', 'magenta');
|
||
|
|
end
|
||
|
|
|
||
|
|
ylabel('Value');
|
||
|
|
xlabel('Sample');
|
||
|
|
|
||
|
|
legend([p1, p2, p3], [strcat('BIAS2 Mean:', {' '}, string(mean(bias2))),...
|
||
|
|
strcat('VAR Mean:', {' '}, string(mean(var))),...
|
||
|
|
strcat('NS Mean:', {' '}, string(mean(var + bias2)))]);
|
||
|
|
hold off
|
||
|
|
end
|