diff --git a/vft.m b/vft.m
index c6938fbd5371fbac2afe2db319463a7b28fdbcd8..5bf4af53689590f7ba4d7ea22ddcc5c90d9b84b9 100644
--- a/vft.m
+++ b/vft.m
@@ -160,10 +160,10 @@ threshold = 1; % to adjust sampling of training thicknesses
% Network parameters
-valexamples = fexamples; % fraction of potential training cells used for validation, (valexamples + maxexamples <= 1)
-numnet = 3; % number of networks to train, network with lowest training error is selected for prediction
-minsector = 1; % minimum number of sectors
-minnodes = 1; % minimum number of hidden nodes
+valexamples = fexamples; % fraction of potential training cells used for validation, (valexamples + maxexamples <= 1)
+numnet = 3; % number of networks to train, network with lowest training error is selected for prediction
+minsector = 1; % minimum number of sectors
+minnodes = 1; % minimum number of hidden nodes
%% PREPROCESSING
MASK.Z(isnan(MASK.Z)) = 0;
DEMc = DEM;MASKc=MASK;
@@ -188,7 +188,7 @@ Ridges = bwareaopen(Ridges,15);
%prepare Train_data and mask_test
Train_data = DEMc.Z;
-ica = find(MASKc.Z == 1); % find cells belonging to the valley-fill
+ica = find(MASKc.Z == 1); % find cells belonging to the valley-fill
Train_catch = Train_data;
Train_data(slope.Z < 10) = 0; % exclude flat areas from training data
Train_data(MASKc.Z == 1) = 0;
@@ -227,7 +227,6 @@ parfor testnum = nrun
% calculate the maximum possible training thickness for each element of y
disp('compute range of possible training thicknesses')
for n = 1 : texamples
- %disp(['compute range of possible training thicknesses ' num2str(n/texamples) '/1 complete'])
nridge = Ridges;
nridge(DEMcZ <= Train_data(y(n))) = 0;
[~,IDX]=bwdist(nridge);
@@ -253,11 +252,10 @@ parfor testnum = nrun
% calculate the maximum possible validation thickness for each element of z
disp('compute range of possible validation thicknesses')
for n = 1 : vexamples
- %disp(['compute range of possible validation thicknesses ' num2str(n/vexamples) '/1 complete'])
nridge = Ridges;
nridge(DEMcZ <= Train_data(z(n))) = 0;
[~,IDX]=bwdist(nridge);
- nrange = DEMcZ(IDX(z(n))) - Train_data(z(n)); % elevation range
+ nrange = DEMcZ(IDX(z(n))) - Train_data(z(n)); % elevation range
nrange_cratev(n,1) = nrange;
end
@@ -268,12 +266,9 @@ parfor testnum = nrun
end
disp('compute distances for training/validation')
for nsector = minsector : maxsector
-% disp(['compute distances for train/validation ' num2str(nsector)])
Storage_Train = zeros(texamples,nsector+addinput); % Distances will be stored here and
Target_Train = zeros(texamples,1); % corresponding thicknesses here
-% disp('training cells')
for n = 1 : texamples
-% disp(['training cells ' num2str(n/texamples) '/1 complete'])
ntarget = randsample(1:nrange_crate(id(n)),1); % randomly sample 1 out of elevation range and
train_elevation = Train_data(y(id(n))) + ntarget; % add it to the training cell elevation to construct a training fill(=target)
mask_train = zeros(size(Train_data));
@@ -331,9 +326,7 @@ parfor testnum = nrun
if validate == 1
Storage_Val = zeros(vexamples,nsector+addinput); % Distances are stored here and
Target_Val = zeros(vexamples,1); % corresponding target thicknesses here
-% disp('validation cells')
for n = 1:vexamples
-% disp(['validation cells ' num2str(n/vexamples) '/1 complete'])
ntarget = randsample(1:nrange_cratev(ik(n)),1); % randomly sample 1 out of elevation range and
train_elevation = Train_datav(z(ik(n))) + ntarget; % add it to the train data elevation to construct a training fill(=target)
mask_vald = zeros(size(Train_datav));
@@ -380,7 +373,7 @@ parfor testnum = nrun
% normalisation
Distance_Val_norm = zeros(size(Storage_Val));
- for col = 1 : size(Distance_val{nsector,testnum},2);
+ for col = 1 : size(Distance_val{nsector,testnum},2)
mean_Distance_train = mean(Distance_train{nsector,testnum}(:,col));
std_Distance_train = std(Distance_train{nsector,testnum}(:,col));
Distance_Val_norm(:,col) = (Distance_val{nsector,testnum}(:,col) - mean_Distance_train) / std_Distance_train;
@@ -423,7 +416,6 @@ parfor testnum = nrun
end
end
-% save([num2str(outdirection),'./temp1.mat']); % save intermediate workspace
%% PREDICTION
% find network configuration that performed best on the validation data set
@@ -455,7 +447,6 @@ parfor i = 1 : size(nrow,1)
end
end
-% save([num2str(outdirection),'./temp2.mat']); % save intermediate workspace
dt = find(StorageFill(:,1)~=0);
Distance_test = StorageFill(any(StorageFill,2),:); % delete zero-rows
@@ -535,31 +526,7 @@ mThickness = MASKc;
mThickness.Z = meanT;
close all
-
-% plot results
-% h = figure;
-% subplot(2,2,1)
-% imagesc(mThickness.Z)
-% colorbar
-% title(['Mean thickness ',num2str(nsector),'-',num2str(nnodes)])
-% subplot(2,2,2)
-% imagesc(stdT)
-% colorbar
-% title('Standard deviation')
-% subplot(2,2,3)
-% imagesc(mRESULTSv)
-% colorbar
-% title('Validation error')
-% subplot(2,2,4)
-% imagesc(Depth_corr.Z)
-% colorbar
-% title('Thickness corrected')
-
totaltime = toc/3600; % total processing time in hours
-% save(['.\',num2str(outdirection),'\varout.mat'],'-v7.3') % save workspace
-% saveas(h,['.\',num2str(outdirection),'\Thickness_',num2str(nsector),'-',num2str(nnodes)],'fig') % save figures
-% runfile = mfilename; % save current file to out folder
-% copyfile(['.\',num2str(runfile),'.m'],['.\',num2str(outdirection)]) % save running code
% write geotiffs
GRIDobj2geotiff(Bed_corr,['.\',num2str(outdirection),'\Bedrock.tif']);