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pixelWise_kNNDataSorting.m
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pixelWise_kNNDataSorting.m
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function sortedDates = pixelWise_kNNDataSorting(maskDir,targetVar,climateVars,addVars,queryDates,learningDates,climateData,additionalVars,normMethods,shortWindow,longWindow,daysRange,Weights,nbImages,metricKNN,optimPrep,saveOptimPrep,parallelComputing,inputDir)
%
%
%
% REDO DOCUMENTATION
%
%
%
%tic
maskData = readgeoraster(maskDir);
% checks that at least one learning and query dates are present
if any(size(learningDates)==0)
error('At least one dimension of LearningDates is 0! Code exited...')
elseif any(size(queryDates)==0)
error('At least one dimension of QueryDates is 0! Code exited...')
end
climateDates = table2array(climateData(:,'date'));
climateMaps = table2array(removevars(climateData,'date'));
%climateVarsNames = string(removevars(climateData,'date').Properties.VariableNames);
queryDatesDate = table2array(queryDates(:,1));
queryDatesData = table2array(queryDates(:,2:end));
learningDatesDate = table2array(learningDates(:,1));
learningDatesData = table2array(learningDates(:,2:end));
if optimPrep == false
% Define learningDates as itself minus the query dates
ismem = ismember(learningDatesDate, queryDatesDate);
learningDatesDate = learningDatesDate(~ismem);
learningDatesData = learningDatesData(~ismem);
end
totQDates = size(queryDatesDate,1);
totLDates = size(learningDatesDate,1);
%totPixels = size(maskData,1) * size(maskData,2);
totGPixels = sum(sum(maskData));
distanceAll = cell(size(maskData,1),size(maskData,2),totQDates);
%sortedDates = cell(totQDates, 1);
%sortedData = cell(totQDates, 1);
%sortedTarget = cell(totQDates, 1);
%sortedAddVars = cell(totQDates, 1);
%sortedDist = cell(totQDates, 1);
if ~isempty(additionalVars)
addVarsDates = table2array(additionalVars(:,'date'));
addVarsData = table2array(removevars(additionalVars,'date'));
else
addVarsDates = [];
addVarsData = [];
end
% Assign different weights
idxTarget = contains(Weights.Properties.VariableNames,targetVar);
weightsTarget = table2array(Weights(:,idxTarget));
idxShort = contains(Weights.Properties.VariableNames,'Short');
weightsShort = table2cell(Weights(:,idxShort));
idxLong = contains(Weights.Properties.VariableNames,'Long');
weightsLong = table2cell(Weights(:,idxLong));
if ~isempty(additionalVars)
idxAddVars = contains(Weights.Properties.VariableNames,addVars);
weightsAddVars = table2cell(Weights(:,idxAddVars));
else
weightsAddVars = [];
end
disp('Starting loop to sort learning dates for each query date...')
% Display progression - for parallel computing
%progress = 0;
%fprintf(1,'Progress: %3.0f%%\n',progress);
%fprintf(['\n' repmat('.',1,totQDates) '\n\n']);
if parallelComputing == true
%% parallel computing
parfor qd = 1:totQDates
currentQDate = queryDatesDate(qd);
dayOfYearQ = day(datetime(currentQDate,'ConvertFrom','yyyyMMdd'),'dayofyear');
minRangeQ = dayOfYearQ - daysRange;
if minRangeQ <= 0, minRangeQ = 365 + minRangeQ; end
maxRangeQ = dayOfYearQ + daysRange;
if maxRangeQ > 365, maxRangeQ = maxRangeQ - 365; end
if minRangeQ < maxRangeQ
rangeQ = minRangeQ:1:maxRangeQ;
else
rangeQmin = minRangeQ:1:365;
rangeQmax = 1:1:maxRangeQ;
rangeQ = [rangeQmin rangeQmax];
end
disp([' Processing day ' num2str(qd) '/' num2str(totQDates) ' (' num2str(currentQDate) ')'])
% Extract the longWindow climate for the current query date
distMapQd = cell(size(maskData));
queryClimate = nan(longWindow, numel(climateVars));
idxQ = find(climateDates == currentQDate);
if idxQ > longWindow
for qxPix = 1:size(maskData,2)
for qyPix = 1:size(maskData,1)
if maskData(qyPix,qxPix) == 1
for k = (longWindow-1):-1:0
queryClimate(k+1,:) = cellfun(@(x) x(qyPix,qxPix), climateMaps(idxQ-k,:));
end
% Extract the additional data for the current query date
if ~isempty(additionalVars)
queryAddVars = cell(1, numel(addVars));
idx = find(addVarsDates == currentQDate);
queryAddVars(1,:) = addVarsData(idx,:);
else
queryAddVars = [];
end
% Compute the distances between the query climate and the climate for each learning date
targetDistance = nan; %nan(totLDates,1);
addVarsDistance = nan; %nan(totLDates,1);
%climateDistance = nan(1,numel(climateVarsNames)); %nan(totLDates,2);
distancePix = nan(totLDates,2);
%climateDistAll = cell(longWindow,numel(climateVars));
% Display progress - only for serial computing
%fprintf(1,' Progress for current query date: %3.0f%%\n',progress);
for ld = 1:totLDates
learningClimate = nan(longWindow, numel(climateVars));
currentLDate = learningDatesDate(ld);
dayOfYearL = day(datetime(currentLDate,'ConvertFrom','yyyyMMdd'),'dayofyear');
idxL = find(climateDates == currentLDate);
%disp([' Computing distance to day ' num2str(l) '/' num2str(totLDates) ' (' num2str(currentLDate) ')'])
if ismember(dayOfYearL,rangeQ) % if learning date is not within 3 months of the query date, it is skipped
if idxL >= longWindow % skips learning dates that are in the longWindow
% Learning dates climate
for k = (longWindow-1):-1:0
learningClimate(k+1,:) = cellfun(@(x) x(qyPix,qxPix), climateMaps(idxL-k,:));
end
% Extract the additional data for the current query date
if ~isempty(additionalVars)
learningAddVars = cell(1, numel(addVars));
idx = find(addVarsDates == currentLDate);
learningAddVars(1,:) = addVarsData(idx,:);
else
learningAddVars = [];
end
% Target variable comparison
if ~(isempty(cell2mat(queryDatesData(qd,:))) || unique(isnan(cell2mat(queryDatesData(qd,:))))) %&& sum(sum(cell2mat(queryDatesData(qd,:))))~=0
if metricKNN == 1 % RMSE
targetDistance(ld) = cellfun(@(x, y) sqrt(mean((x - y).^2, 'all', 'omitnan')), ...
queryDatesData(qd,:), learningDatesData(ld,:), 'UniformOutput', false); % RMSE
elseif metricKNN == 2 % MAE
targetDistance(ld) = cellfun(@(x, y) mean(abs(x - y), 'all', 'omitnan'), ...
queryDatesData(qd,:), learningDatesData(ld,:), 'UniformOutput', false); % MAE
elseif metricKNN == 3 % Manhattan
targetDistance(ld) = cellfun(@(x, y) sum(abs(x - y), 'all', 'omitnan'), ...
queryDatesData(qd,:), learningDatesData(ld,:), 'UniformOutput', false); % Manhattan
elseif metricKNN == 4 % Euclidean
targetDistance(ld) = cellfun(@(x, y) sqrt(sum((x - y).^2, 'all', 'omitnan')), ...
queryDatesData(qd,:), learningDatesData(ld,:), 'UniformOutput', false); % Euclidean
elseif metricKNN == 5 % SPEM
targetDistance(ld) = cellfun(@(x, y) spem(x, y), ...
queryDatesData(qd,:), learningDatesData(ld,:));
else
error('Bad metricKNN parameter')
end
targetDistance(ld) = sum(cell2mat(targetDistance(ld)),1,'omitnan');
if optimPrep == false
targetDistance(ld) = targetDistance(ld).*weightsTarget;
targetDistance(ld) = sum(cell2mat(targetDistance(ld)),2,'omitnan');
end
else
targetDistance = 0;
end
% Additional variable comparison
% 1 distance
if ~isempty(addVars) && ~isempty(addVarsData)
if ~isempty(addVarsData(qd,:))
if metricKNN == 1 % RMSE
addVarsDistance(ld) = cellfun(@(x, y) sqrt(mean((x - y).^2, 'all', 'omitnan')), ...
queryAddVars, learningAddVars, 'UniformOutput', false); % RMSE
elseif metricKNN == 2 % MAE
addVarsDistance(ld) = cellfun(@(x, y) mean(abs(x - y), 'all', 'omitnan'), ...
queryAddVars, learningAddVars, 'UniformOutput', false); % MAE
elseif metricKNN == 3 % Manhattan
addVarsDistance(ld) = cellfun(@(x, y) sum(abs(x - y), 'all', 'omitnan'), ...
queryAddVars, learningAddVars, 'UniformOutput', false); % Manhattan
elseif metricKNN == 4 % Euclidean
addVarsDistance(ld) = cellfun(@(x, y) sqrt(sum((x - y).^2, 'all', 'omitnan')), ...
queryAddVars, learningAddVars, 'UniformOutput', false); % Euclidean
elseif metricKNN == 5 % SPEM
addVarsDistance(ld) = cellfun(@(x, y) spem(x, y), ...
queryAddVars, learningAddVars);
else
error('Bad metricKNN parameter')
end
addVarsDistance(ld) = sum(cell2mat(addVarsDistance(ld)),1,'omitnan');
if optimPrep == false
if numel(addVars) == 1
addVarsDistance(ld) = addVarsDistance(ld) .* cell2mat(weightsAddVars);
else
addVarsDistance(ld) = num2cell(cell2mat(addVarsDistance(ld)) .* cell2mat(weightsAddVars));
end
end
end
else
addVarsDistance = 0;
end
% Climate distance
% climateDistance = 1 - corr(learningClimate,queryClimate);
if metricKNN == 1 % RMSE
climateDistance = sqrt(mean(learningClimate - queryClimate).^2);
elseif metricKNN == 2 % MAE
climateDistance = mean(abs(learningClimate - queryClimate));
elseif metricKNN == 3 % Manhattan
climateDistance = sum(abs(learningClimate - queryClimate));
elseif metricKNN == 4 % Euclidean
climateDistance = sqrt(sum((learningClimate - queryClimate).^2));
elseif metricKNN == 5 % SPEM
climateDistance = spem(learningClimate, queryClimate);
else
error('Bad metricKNN parameter')
end
distancePix(ld,1) = currentLDate;
distancePix(ld,2) = sum(climateDistance) + targetDistance + addVarsDistance;
if distancePix(ld,2) == 0
distancePix(ld,2) = distancePix(ld,2) + eps; % VERY STRANGE, CHECK WHY 0 SOMETIMES <--------------------------------------------------------------------------------------------------------------
end
else
continue
end
else
% If not enough climate days available, skip until loop reaches longWindow
%warning(['Climate data available is shorter than longWindow, ' num2str(currentLDate) ' skipped.'])
continue
end
end
distancePix = sortrows(distancePix(~isnan(distancePix(:,2)),:),2);
distancePix = distancePix(1:nbImages,:);
distMapQd{qyPix,qxPix} = distancePix;
else
% If learning date not in query date range, skip it
%disp([' Learning day ',num2str(currentLDate),' not in query date range, skipped'])
continue
end
end
end
else
fprintf('\n')
disp(' Not enough learning dates. Query date skipped...')
fprintf('\b')
continue
end
distanceAll(:,:,qd) = distMapQd;
% % Learning dates distance: 1 date, 2 distance
% distance = climateDistance(~cellfun('isempty',climateDistance(:,1)),:);
% targetDistance = targetDistance(~cellfun('isempty',targetDistance),:);
% addVarsDistance = addVarsDistance(~cellfun('isempty',addVarsDistance),:);
% if optimPrep == false
% distancesSort = sortrows(distance,2); % Sort rows in ascending order according to column 2
% distancesBest = distancesSort(1:nbImages,1);
% distSorted = distancesSort(1:nbImages,2);
% sortedDates{qd} = currentQDate;
% sortedData{qd} = cell2mat(distancesBest);
% sortedDist{qd} = cell2mat(distSorted);
% else
% distancesBest = distance(:,1);
% distSorted = distance(:,2);
% sortedDates{qd} = currentQDate;
% sortedData{qd} = distancesBest;
% sortedTarget{qd} = targetDistance;
% sortedAddVars{qd} = addVarsDistance;
% sortedDist{qd} = distSorted;
% end
end
% if optimPrep == false
% sortedDatesAll = [sortedDates sortedData sortedDist];
% sortedDates = sortedDatesAll;
% else
% sortedDatesAll = [sortedDates sortedData sortedTarget sortedAddVars sortedDist];
% sortedDates = sortedDatesAll;
% end
sortedDates.data = distanceAll;
sortedDates.date = queryDatesDate;
% Shut down parallel pool
poolobj = gcp('nocreate');
delete(poolobj);
else
%% serial computing
for qd = 1:totQDates
currentQDate = queryDatesDate(qd);
dayOfYearQ = day(datetime(currentQDate,'ConvertFrom','yyyyMMdd'),'dayofyear');
minRangeQ = dayOfYearQ - daysRange;
if minRangeQ <= 0, minRangeQ = 365 + minRangeQ; end
maxRangeQ = dayOfYearQ + daysRange;
if maxRangeQ > 365, maxRangeQ = maxRangeQ - 365; end
if minRangeQ < maxRangeQ
rangeQ = minRangeQ:1:maxRangeQ;
else
rangeQmin = minRangeQ:1:365;
rangeQmax = 1:1:maxRangeQ;
rangeQ = [rangeQmin rangeQmax];
end
fprintf(['\n Processing day ' num2str(qd) '/' num2str(totQDates) ' (' num2str(currentQDate) ')'])
% Extract the longWindow climate for the current query date
distMapQd = cell(size(maskData));
queryClimate = nan(longWindow, numel(climateVars));
idxQ = find(climateDates == currentQDate);
if idxQ > longWindow
% Display progress - only for serial computing
cPix = 0;
progress = 0;
fprintf(1,'\n Progress for current query date: %3.0f%%\n',progress);
for qxPix = 1:size(maskData,2)
for qyPix = 1:size(maskData,1)
if maskData(qyPix,qxPix) == 1
for k = (longWindow-1):-1:0
queryClimate(k+1,:) = cellfun(@(x) x(qyPix,qxPix), climateMaps(idxQ-k,:));
end
% Extract the additional data for the current query date
if ~isempty(additionalVars)
queryAddVars = cell(1, numel(addVars));
idxQ = find(addVarsDates == currentQDate);
queryAddVars(1,:) = addVarsData(idxQ,:);
else
queryAddVars = [];
end
% Compute the distances between the query climate and the climate for each learning date
%targetDistance = cell(totLDates,1);
addVarsDistance = cell(totLDates,1);
%climateDistance = nan(totLDates,2);
distancePix = nan(totLDates,2);
%climateDistAll = cell(longWindow,numel(climateVars));
% Display progress - only for serial computing
%progress = 0;
%fprintf(1,'\n Progress for current query date: %3.0f%%\n',progress);
for ld = 1:totLDates
learningClimate = nan(longWindow, numel(climateVars));
currentLDate = learningDatesDate(ld);
dayOfYearL = day(datetime(currentLDate,'ConvertFrom','yyyyMMdd'),'dayofyear');
idxL = find(climateDates == currentLDate);
%disp([' Computing distance to day ' num2str(l) '/' num2str(totLDates) ' (' num2str(currentLDate) ')'])
if ismember(dayOfYearL,rangeQ) % if learning date is not within 3 months of the query date, it is skipped
%disp([' Processing learning day ', num2str(currentLDate)])
if idxL >= longWindow % skips learning dates that are in the longWindow
% Learning dates climate
for k = (longWindow-1):-1:0
learningClimate(k+1,:) = cellfun(@(x) x(qyPix,qxPix), climateMaps(idxL-k,:));
end
% Extract the additional data for the current query date
if ~isempty(additionalVars)
learningAddVars = cell(1, numel(addVars));
idx = find(addVarsDates == currentLDate);
learningAddVars(1,:) = addVarsData(idx,:);
else
learningAddVars = [];
end
% Target variable comparison
if ~(isempty(cell2mat(queryDatesData(qd,:))) || unique(isnan(cell2mat(queryDatesData(qd,:))))) %&& sum(sum(cell2mat(queryDatesData(qd,:))))~=0
if metricKNN == 1 % RMSE
targetDistance = cellfun(@(x, y) sqrt(mean((x - y).^2, 'all', 'omitnan')), ...
queryDatesData(qd,:), learningDatesData(ld,:), 'UniformOutput', false); % RMSE
elseif metricKNN == 2 % MAE
targetDistance = cellfun(@(x, y) mean(abs(x - y), 'all', 'omitnan'), ...
queryDatesData(qd,:), learningDatesData(ld,:), 'UniformOutput', false); % MAE
elseif metricKNN == 3 % Manhattan
targetDistance = cellfun(@(x, y) sum(abs(x - y), 'all', 'omitnan'), ...
queryDatesData(qd,:), learningDatesData(ld,:), 'UniformOutput', false); % Manhattan
elseif metricKNN == 4 % Euclidean
targetDistance = cellfun(@(x, y) sqrt(sum((x - y).^2, 'all', 'omitnan')), ...
queryDatesData(qd,:), learningDatesData(ld,:), 'UniformOutput', false); % Euclidean
elseif metricKNN == 5 % SPEM
targetDistance = cellfun(@(x, y) spem(x, y), ...
queryDatesData(qd,:), learningDatesData(ld,:));
else
error('Bad metricKNN parameter')
end
targetDistance = sum(cell2mat(targetDistance),1,'omitnan');
if optimPrep == false
targetDistance = targetDistance.*weightsTarget;
targetDistance = sum(cell2mat(targetDistance(ld)),2,'omitnan');
end
else
targetDistance = 0;
end
% Additional variable comparison
% 1 distance
if ~isempty(additionalVars) && ~isempty(cell2mat(addVarsData(qd,:)))
if metricKNN == 1 % RMSE
addVarsDistance{ld} = cellfun(@(x, y) sqrt(mean((x - y).^2, 'all', 'omitnan')), ...
queryAddVars, learningAddVars, 'UniformOutput', false); % RMSE
elseif metricKNN == 2 % MAE
addVarsDistance{ld} = cellfun(@(x, y) mean(abs(x - y), 'all', 'omitnan'), ...
queryAddVars, learningAddVars, 'UniformOutput', false); % MAE
elseif metricKNN == 3 % Manhattan
addVarsDistance{ld} = cellfun(@(x, y) sum(abs(x - y), 'all', 'omitnan'), ...
queryAddVars, learningAddVars, 'UniformOutput', false); % Manhattan
elseif metricKNN == 4 % Euclidean
addVarsDistance{ld} = cellfun(@(x, y) sqrt(sum((x - y).^2, 'all', 'omitnan')), ...
queryAddVars, learningAddVars, 'UniformOutput', false); % Euclidean
elseif metricKNN == 5 % SPEM
addVarsDistance{ld} = cellfun(@(x, y) spem(x, y), ...
queryAddVars, learningAddVars);
else
error('Bad metricKNN parameter')
end
addVarsDistance{ld} = sum(cell2mat(addVarsDistance{ld}),1,'omitnan');
if optimPrep == false
if numel(addVars) == 1
addVarsDistance{ld} = addVarsDistance{ld} .* cell2mat(weightsAddVars);
else
addVarsDistance{ld} = cell2mat(addVarsDistance{ld}) .* cell2mat(weightsAddVars);
end
end
else
addVarsDistance = 0;
end
% Climate distance
% for climVar = 1:numel(climateVars)
% climateDistance(:,climVar) = corr(learningClimate(:,climVar),queryClimate(:,climVar));
% end
if metricKNN == 1 % RMSE
climateDistance = sqrt(mean(learningClimate - queryClimate).^2);
elseif metricKNN == 2 % MAE
climateDistance = mean(abs(learningClimate - queryClimate));
elseif metricKNN == 3 % Manhattan
climateDistance = sum(abs(learningClimate - queryClimate));
elseif metricKNN == 4 % Euclidean
climateDistance = sqrt(sum((learningClimate - queryClimate).^2));
elseif metricKNN == 5 % SPEM
climateDistance = spem(learningClimate, queryClimate);
else
error('Bad metricKNN parameter')
end
distancePix(ld,1) = currentLDate;
distancePix(ld,2) = sum(climateDistance) + targetDistance + addVarsDistance;
if distancePix(ld,2) == 0
distancePix(ld,2) = distancePix(ld,2) + eps; % VERY STRANGE, CHECK WHY 0 SOMETIMES <--------------------------------------------------------------------------------------------------------------
end
% climateDistance{ld,1} = currentLDate;
% if shortWindow > 0
% climateDistance{ld,2}(1,:) = sum(cell2mat(climateDistAll(1:shortWindow,:)),1,'omitnan');
% else
% climateDistance{ld,2}(1,:) = single(zeros(1,size(climateDistAll,2)));
% end
% climateDistance{ld,2}(2,:) = sum(cell2mat(climateDistAll(shortWindow+1:end,:)),1,'omitnan');
% % Assign weights to corresponding index
% if optimPrep == false
% climateDistance{ld,2}(1,:) = climateDistance{ld,2}(1,:) .* cell2mat(weightsShort);
% climateDistance{ld,2}(2,:) = climateDistance{ld,2}(2,:) .* cell2mat(weightsLong);
% climateDistance{ld,2} = sum(climateDistance{ld,2},1,'omitnan');
% climateDistance{ld,2} = sum(climateDistance{ld,2},2,'omitnan')+targetDistance{ld}+addVarsDistance{ld};
% end
else
% If not enough climate days available, skip until loop reaches longWindow
%warning(['Climate data available is shorter than longWindow, ' num2str(currentLDate) ' skipped.'])
continue
end
else
% If learning date not in query date range, skip it
% Display computation progress - only for serial computing
%progress = (100*(ld/totLDates));
%fprintf(1,'\b\b\b\b%3.0f%%',progress);
%disp([' Learning day ',num2str(currentLDate),' not in query date range, skipped'])
continue
end
end
distancePix = sortrows(distancePix(~isnan(distancePix(:,2)),:),2);
distancePix = distancePix(1:nbImages,:);
distMapQd{qyPix,qxPix} = distancePix;
else
continue
end
cPix = cPix + 1;
end
% Display computation progress - only for serial computing
%progress = (100*(ld/totLDates));
progress = 100*(cPix/totGPixels);
fprintf(1,'\b\b\b\b%3.0f%%',progress);
end
else
fprintf('\n')
disp(' Not enough learning dates. Query date skipped...')
fprintf('\b')
continue
end
distanceAll(:,:,qd) = distMapQd;
% % Learning dates distance: 1 date, 2 distance
% distance = climateDistance(~cellfun('isempty',climateDistance(:,1)),:);
% targetDistance = targetDistance(~cellfun('isempty',targetDistance),:);
% addVarsDistance = addVarsDistance(~cellfun('isempty',addVarsDistance),:);
% if optimPrep == false
% distancesSort = sortrows(distance,2); % Sort rows in ascending order according to column 2
% distancesBest = distancesSort(1:nbImages,1);
% distSorted = distancesSort(1:nbImages,2);
% sortedDates{qd} = currentQDate;
% sortedData{qd} = cell2mat(distancesBest);
% sortedDist{qd} = cell2mat(distSorted);
% else
% distancesBest = distance(:,1);
% distSorted = distance(:,2);
% sortedDates{qd} = currentQDate;
% sortedData{qd} = distancesBest;
% sortedTarget{qd} = targetDistance;
% sortedAddVars{qd} = addVarsDistance;
% sortedDist{qd} = distSorted;
% end
% Display progression - for parallel computing
%progress = (100*(l/totLDates));
%fprintf(1,'\b\b\b\b%3.0f%%',progress);
%toc
end
% if optimPrep == false
% sortedDatesAll = [sortedDates sortedData sortedDist];
% sortedDates = sortedDatesAll;
% else
% sortedDatesAll = [sortedDates sortedData sortedTarget sortedAddVars sortedDist];
% sortedDates = sortedDatesAll;
% end
sortedDates.data = distanceAll;
sortedDates.date = queryDatesDate;
fprintf('\n')
end
%sortedDates = sortedDates(~cellfun('isempty',sortedDates(:,1)),:);
if optimPrep == false
disp('Saving KNNSorting.mat file...')
save(fullfile(inputDir,'KNNSorting.mat'),'sortedDates', '-v7.3','-nocompression'); % Save Ranked Learning Dates per Query Date
else
if saveOptimPrep == true
disp('Saving KNNDistances.mat file for optimisation. May take a while...')
save(fullfile(inputDir,'KNNDistances.mat'),'sortedDates', '-v7.3','-nocompression');
end
end
%toc
end