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mwakokmwakok
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Initialization of repo based on initial MatLab code base
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‎Case study.xlsx‎

16.4 KB
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‎allpaths.m‎

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function p = allpaths(A,start,last)
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% find all direct paths from start to last
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% A is (n x 2) each row is an edges
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A = sortrows(A);
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b = true(size(A,1),1);
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p = gapengine(A,b,start,last);
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end
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function p = gapengine(A,b,start,last)
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% recursive engine
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if start==last
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p = {last};
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else
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bs = A(:,1) == start;
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next = A(bs & b,2);
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p = {};
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b(bs) = false;
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for k=1:length(next)
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i = next(k);
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pk = gapengine(A,b,i,last);
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pk = cellfun(@(p) [start, p], pk, 'unif', 0);
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p = [p, pk];
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end
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end
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end

‎cost_cdfdist.m‎

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function pd1 = cost_cdfdist(c1,c2)
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% Force all inputs to be column vectors
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c1 = c1(:);
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c2 = c2(:);
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% Prepare figure
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clf;
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hold on;
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LegHandles = []; LegText = {};
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% --- Plot data originally in dataset "c1 data"
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% This dataset does not appear on the plot
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% Get data limits to determine plotting range
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XLim = [0.8*min(c1),1.2*max(c1)];
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% Create grid where function will be computed
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XLim = XLim + [0 1] * 0.01 * diff(XLim);
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XGrid = linspace(XLim(1),XLim(2),100);
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% --- Create fit "fit 1"
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pd1 = fitdist(c1,'kernel','kernel','normal','support','unbounded');
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YPlot1 = cdf(pd1,XGrid);
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hLine = plot(XGrid,YPlot1,'Color',[0 0 0],...
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'LineStyle','-', 'LineWidth',2,...
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'Marker','none', 'MarkerSize',6);
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LegHandles(end+1) = hLine;
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LegText{end+1} = 'Tentative cost CDF';
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hold on
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a=find(YPlot1>0.999);
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if length(a)>0
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a=a(1);
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text(XGrid(a),0.96,1,strcat('cost=',num2str(round(XGrid(a)))),'Color','red','FontSize',14);
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plot(XGrid(a),1,'o','MarkerSize',10,'LineWidth',2);
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end
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hold on
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% --- Plot data originally in dataset "c2 data"
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% This dataset does not appear on the plot
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% Get data limits to determine plotting range
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XLim = [0.8*min(c2), 1.2*max(c2)];
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% Create grid where function will be computed
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XLim = XLim + [0 1] * 0.01 * diff(XLim);
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XGrid = linspace(XLim(1),XLim(2),100);
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xlabel('cost (Euros)','FontSize',20)
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ylabel('Cumulative probability','FontSize',20)
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bx = gca;
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bx.FontSize = 20;
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% --- Create fit "fit 2"
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pd2 = fitdist(c2,'kernel','kernel','normal','support','unbounded');
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YPlot2 = cdf(pd2,XGrid);
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hLine = plot(XGrid,YPlot2,'Color',[0.7 0.7 0.7],...
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'LineStyle','-', 'LineWidth',2,...
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'Marker','none', 'MarkerSize',6);
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LegHandles(end+1) = hLine;
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LegText{end+1} = 'Permanent cost CDF';
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% Adjust figure
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box on;
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grid on;
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hold off;
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% Create legend from accumulated handles and labels
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hLegend = legend(LegHandles,LegText,'Orientation', 'vertical', 'FontSize', 18, 'Location', 'northwest');
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set(hLegend,'Interpreter','none');

‎cost_pdfdist.m‎

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function pd1 = cost_pdfdist(c1,c2)
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% Force all inputs to be column vectors
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c1 = c1(:);
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c2 = c2(:);
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% Prepare figure
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clf;
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hold on;
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LegHandles = []; LegText = {};
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%%
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% Get data limits to determine plotting range
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XLim = [0.8*min(c1),1.2*max(c1)];
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% Create grid where function will be computed
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XLim = XLim + [0 1] * 0.01 * diff(XLim);
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XGrid = linspace(XLim(1),XLim(2),100);
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%%
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% --- Plot data originally in dataset "c1 data"
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[CdfF,CdfX] = ecdf(c1,'Function','cdf'); % compute empirical cdf
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BinInfo.rule = 1;
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[~,BinEdge] = internal.stats.histbins(c1,[],[],BinInfo,CdfF,CdfX);
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[BinHeight,BinCenter] = ecdfhist(CdfF,CdfX,'edges',BinEdge);
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hLine = bar(BinCenter,BinHeight,'hist');
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set(hLine,'FaceColor','none','EdgeColor',[0 0 0],...
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'LineStyle','-', 'LineWidth',1);
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LegHandles(end+1) = hLine;
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LegText{end+1} = 'Tentative cost histogram';
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hold on
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%%
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% --- Create fit "pdf1"
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pd1 = fitdist(c1,'kernel','kernel','normal','support','unbounded');
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YPlot = pdf(pd1,XGrid);
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hLine = plot(XGrid,YPlot,'Color',[0 0 0],...
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'LineStyle','-', 'LineWidth',3,...
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'Marker','none', 'MarkerSize',6);
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LegHandles(end+1) = hLine;
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LegText{end+1} = 'Tentative cost PDF';
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hold on
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%%
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% Get data limits to determine plotting range
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XLim = [0.8*min(c2), 1.2*max(c2)];
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% Create grid where function will be computed
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XLim = XLim + [0 1] * 0.01 * diff(XLim);
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XGrid = linspace(XLim(1),XLim(2),100);
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%%
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% --- Plot data originally in dataset "c2 data"
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[CdfF,CdfX] = ecdf(c2,'Function','cdf'); % compute empirical cdf
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BinInfo.rule = 1;
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[~,BinEdge] = internal.stats.histbins(c2,[],[],BinInfo,CdfF,CdfX);
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[BinHeight,BinCenter] = ecdfhist(CdfF,CdfX,'edges',BinEdge);
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hLine = bar(BinCenter,BinHeight,'hist');
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set(hLine,'FaceColor','none','EdgeColor',[0.7 0.7 0.7],...
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'LineStyle','-', 'LineWidth',1);
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xlabel('cost (Euros)','FontSize',20)
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ylabel('PDF','FontSize',20)
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LegHandles(end+1) = hLine;
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LegText{end+1} = 'Permanent cost histogram';
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bx = gca;
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bx.FontSize = 20;
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hold on
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%%
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% --- Create fit "pdf2"
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pd2 = fitdist(c2,'kernel','kernel','normal','support','unbounded');
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YPlot = pdf(pd2,XGrid);
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hLine = plot(XGrid,YPlot,'Color',[0.7 0.7 0.7],...
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'LineStyle','-', 'LineWidth',2,...
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'Marker','none', 'MarkerSize',6);
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LegHandles(end+1) = hLine;
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LegText{end+1} = 'Permanent cost PDF';
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% Adjust figure
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box on;
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hold off;
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% Create legend from accumulated handles and labels
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hLegend = legend(LegHandles,LegText,'Orientation', 'vertical', 'FontSize', 20, 'Location', 'northeast');
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set(hLegend,'Interpreter','none');

‎fitting.m‎

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function fitting(y_opt,y_all,y_0,y_0_nouncertainty,T_pl)
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rng('default'); % for reproducibility
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% Force all inputs to be column vectors
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y_opt = y_opt(:);
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y_all = y_all(:);
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y_0 = y_0(:);
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y_0_nouncertainty=y_0_nouncertainty(:)+1;
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% Prepare figure
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clf;
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hold on;
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LegHandles = []; LegText = {};
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%Data boundaries adjustment
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[CdfY,CdfX] = ecdf(y_all,'Function','cdf'); % compute empirical function
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a=CdfX(1)-2;
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b=CdfY(1);
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[CdfY,CdfX] = ecdf(y_0,'Function','cdf'); % compute empirical function
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c=CdfX(end)+2;
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d=CdfY(end);
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%%
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% --- Plot data originally in dataset "y_0_nouncertainty data"
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[CdfY,CdfX] = ecdf(y_0_nouncertainty,'Function','cdf'); % compute empirical function
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CdfX=[a;CdfX;c];
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CdfY=[b;CdfY;d];
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hLine = plot(CdfX,CdfY,'lineWidth',2,'Color' , [0.7 0.7 0.7],'DisplayName','Orig Dur');
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hold on;
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%%
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% --- Plot data originally in dataset "y_0 data"
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[CdfY,CdfX] = ecdf(y_0,'Function','cdf'); % compute empirical function
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%hLine = stairs(CdfX,CdfY,'Color',[0 0 0],'LineStyle','-', 'LineWidth',1);
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%hold on;
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% --- Create fit for "y_0 data"
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CdfX(1)=CdfX(1)-1;
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CdfX=[a;CdfX;c];
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CdfY=[b;CdfY;d];
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x=CdfX;
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xq2 = linspace(CdfX(1),CdfX(end),200);
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p1 = pchip(x,CdfY,xq2);
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plot(xq2,p1,':','lineWidth',2,'Color' , [0.7 0.7 0.7],'DisplayName','NoMit')
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hold on
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%%
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% --- Plot data originally in dataset "y_all data"
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[CdfY,CdfX] = ecdf(y_all,'Function','cdf'); % compute empirical function
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%hLine = stairs(CdfX,CdfY,'Color',[0.333333 0.666667 0],'LineStyle','-', 'LineWidth',1);
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% --- Create fit for "y_all data"
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CdfX(1)=CdfX(1)-1;
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CdfX=[a;CdfX;c];
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CdfY=[b;CdfY;d];
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x=CdfX;
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xq2 = linspace(CdfX(1),CdfX(end),200);
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p2 = pchip(x,CdfY,xq2);
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plot(xq2,p2,'--','lineWidth',2, 'Color' , [0.7 0.7 0.7],'DisplayName','Permanent')
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hold on;
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%%
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% --- Plot data originally in dataset "y_opt data"
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[CdfY,CdfX] = ecdf(y_opt,'Function','cdf'); % compute empirical function
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%hLine = stairs(CdfX,CdfY,'Color',[0.333333 0 0.666667],'LineStyle','-', 'LineWidth',1);
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% --- Create fit for "y_opt data"
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CdfX(1)=CdfX(1)-1;
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CdfX=[a;CdfX;c];
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CdfY=[b;CdfY;d];
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x=CdfX;
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xq2 = linspace(CdfX(1),CdfX(end),200);
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p3 = pchip(x,CdfY,xq2);
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plot(xq2,p3,'lineWidth',2,'Color' , 'k','DisplayName','Tentative')
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hold on;
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a=find(x>=T_pl);
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if length(a)>0
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a=a(1);
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text(T_pl,CdfY(a)-0.05,1,strcat('Target duration=',num2str(T_pl)),'Color','red','FontSize',14);
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plot(T_pl+1,CdfY(a)+0.006,'o','Color','red','MarkerSize',10,'LineWidth',2,'DisplayName','Planned duration');
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end
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%%
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% Create grid where function will be computed
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XLim = get(gca,'XLim');
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XLim = XLim + [-1 1] * 0.01 * diff(XLim);
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XGrid = linspace(XLim(1),XLim(2),800);
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xlabel('Duration (days)','FontSize',20)
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ylabel('Cumulative probability','FontSize',20)
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bx = gca;
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bx.FontSize = 20;
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% Adjust figure
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box on;
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hold off;
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% Create legend from accumulated handles and labels
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legend('Orig Dur','No Mit','Permanent','Tentative');
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hLegend = legend(LegHandles,LegText,'Orientation', 'vertical', 'FontSize', 18, 'Location', 'northeast');
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set(hLegend,'Interpreter','none');
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grid on

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