MATLAB: Unable to generate same plot in MATLAB as generated in Curve Fitting tool app

curve fitting

Hi,

I am trying to plot a graph between Power and speed. Unfortunately, I have data for for only high speed but I do know that the relationship between power and speed of a ship is a cubic curve. So, I decided to use the curve fitting tool app in MATLAB to predict power at lower speeds. However, I am having a hard time exporting the plot exactly as it is from curve fiiting tool to MATLAB. I am not able to get the data for lower speeds. Please find attached the plots in MATLAB and Curve Fit. Tool.

How can I export the plot as it is to MATLAB so that I have power for lower speeds?

Best Answer

Use the File>Print to Figure menu option.

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MATLAB: Generate sequence

Here's the only way I think you can do your particular choice set - don't bother storing the choices in memory, just print them to the screen:

function solutionsFound = perms_bounded(choices, minBounds, maxBounds)
% Since we're only printing solutions, let's use strings
if ~iscellstr(choices)
    choices = arrayfun(@num2str, choices, 'UniformOutput',false);
end
% Which choice indices can be placed in each location?
validInds = arrayfun(@(x)find(minBounds<=x & maxBounds>=x),1:length(choices),'UniformOutput',false);
setLens = cellfun(@numel,validInds);
n = length(setLens);
iSet = zeros(1, n); % Which indices of each indice set are we up to?
candSet = zeros(1,n); % What's the combination we have so far?
solutionsFound = 0;
rLevel = 1;
while 1
    iSet(rLevel) = iSet(rLevel)+1;
    if iSet(rLevel)>setLens(rLevel)
        % We've run out of indices for this location. Exit if we're
        % finished, otherwise go back one rLevel and start again.
        if all(iSet>=setLens), break,  end
        iSet(rLevel) = 0;  rLevel = rLevel-1; continue;
    end
    candidate = validInds{rLevel}(iSet(rLevel));
    % Has this candidate already been used?
    if any(candSet(1:rLevel-1)==candidate)
        continue; % Already in use, try the next candidate. 
    else
        % Place this candidate and move to the next level
        candSet(rLevel) = candidate;
        if rLevel<n
            rLevel = rLevel+1;
        else
            solutionsFound = solutionsFound+1;
            fprintf('#%d: %s\n',solutionsFound, sprintf('%s ',choices{candSet}))
        end
    end
end

Using the small set we get:

    choices = {'A','B','C','D'};
    minBounds = [1 1 2 3];
    maxBounds = [4 3 4 4];
    >> numSolutions = perms_bounded(choices, minBounds, maxBounds)
    #1: A B C D 
    #2: A B D C 
    #3: A C B D 
    #4: B A C D 
    #5: B A D C 
    #6: B C A D 
    #7: B C D A 
    numSolutions =
         7

Using the large set we get:

>> perms_bounded(choices, minBounds, maxBounds)
#1: a b c d e f g h i j k l m n o p q r s t u v w x y z aa ab ac ad ae af ag ah ai aj ak al am an ao ap aq ar as at au av aw ax ay 
#2: a b c d e f g h i j k l m n o p q r s t u v w x y z aa ab ac ad ae af ag ah ai aj ak al am an ao ap aq as ar at au av aw ax ay 
#3: a b c d e f g h i j k l m n o p q r s t u v w x y z aa ab ac ad ae af ag ah ai aj ak al am an ao ap ar aq as at au av aw ax ay 
#4: a b c d e f g h i j k l m n o p q r s t u v w x y z aa ab ac ad ae af ag ah ai aj ak al am an ao ap ar as aq at au av aw ax ay 
...
#49998: a b c d e f g h i j k l m n o p q r s t u v w x y aa z ab ac ag ad ae af ah ai al aj ak am an aq ao ap as ar at au av aw ax ay 
#49999: a b c d e f g h i j k l m n o p q r s t u v w x y aa z ab ac ag ad ae af ah ai al aj ak am aq an ao ap ar as at au av aw ax ay 
#50000: a b c d e f g h i j k l m n o p q r s t u v w x y aa z ab ac ag ad ae af ah ai al aj ak am aq an ao ap as ar at au av aw ax ay 
#50001: a b c d e f g h i j k l m n o p q r s t u v w x y aa z ab ac ag ad ae af ah ai al aj ak aq am an ao ap ar as at au av aw ax ay 
...

I've truncated the output after 50000 valid solutions. Note that choices a-through-y are still on their first valid combination. If you want to see all solutions, you may need a few weeks for it to run.

Thanks, Sven.

MATLAB: Matlab curve fitting tool automation for selection of fit with minimum sse.

You could certainly write your own function by using the gof output of the "fit" function.

Here is an example that shows you how to use the "fit" to see the Goodness of Fit of different methods. You could store the "gof" values in an array and find the max GoF.

function bestFitName = Gof_Finder( x, y )
    Fits = {'poly1', 'poly2', 'poly3', 'exp1'};
      for i=1:numel(Fits)
          [~,GoFs(i)] = fit( x, y,  Fits{i} );
      end
      [~, minIdx] = min([GoFs.sse]);
      bestFitName = Fits{minIdx};
  end

The above function will return the name of the best fit of the data x and y.

Best Answer

Related Solutions

MATLAB: Generate sequence

MATLAB: Matlab curve fitting tool automation for selection of fit with minimum sse.

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