Graphing Base Form of y=mx+b from a .csv file

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Nathan Rivard 2019-10-15

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编辑： Adam Danz 2019-10-16

I have a few thousand equations in y=mx+b form that I have acquired from hundreds of thousands of data points in multiple excel files. They are all trendlines of similar T-s data. I am attempting to create generalized trendlines of similar data types and I would like to be able to select formulas in y=mx+b form that I would like plotted, and then find the formula for the LOBF (Line of best fit) for that set of equations. I am new to MatLab and would just like some assistance in this.

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Nathan Rivard 2019-10-15

The data was already imported through the import data tool and is sitting in a table of variables in MatLab. I got rid of the first column of numeric data as well but I will continue work onto tomorrow and post an update. Thank you for the help

Adam Danz 2019-10-15

编辑：Adam Danz 2019-10-16

That info is helpful. If you're reading it in as a table and you already got rid of the first column, the first line of my solution will be

numVals = regexp(data{:,:},'[+-]?\d+\.?\d*', 'match');
%                    ^^^^^ add this

Alternatively, if you hadn't removed the first column, you could have done,

numVals = regexp(data{:,2:end},'[+-]?\d+\.?\d*', 'match');
%                    ^^^^^^^^^

The rest of my solution will be the same.

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采纳的回答

Adam Danz 2019-10-15

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/485532-graphing-base-form-of-y-mx-b-from-a-csv-file#answer_396520

I'm assuming you've read the data into Matlab and you've got a cell array of strings or character arrays named "data" that appears like this:

data =
  3×6 cell array
  Columns 1 through 3
    {'y = 2.3297x + 1…'}    {'y = 1.8866x + 1…'}    {'y = 0.3373x + 1…'}
    {'y = 1.9526x + 9…'}    {'y = 1.7754x + 9…'}    {'y = 2.774x + 68…'}
    {'y = 1.9004x + 9…'}    {'y = 1.9833x + 8…'}    {'y = 3.1304x + 6…'}
  Columns 4 through 6
    {'y = 3.584x + 69…'}    {'y = 2.1044x + 6…'}    {'y = 0.0704x + 6…'}
    {'y = 3.0042x + 6…'}    {'y = 2.3015x + 5…'}    {'y = 0.1415x + 6…'}
    {'y = 3.1099x + 6…'}    {'y = 2.466x + 60…'}    {'y = 0.2486x + 6…'}

Execute these two lines below to extract the slope and intercept values from each string.

numVals = regexp(data,'[+-]?\d+\.?\d*', 'match');
mb = cellfun(@str2double, numVals, 'UniformOutput', false); 

mb is a cell array the same size as "data" and contains the [slope,intercept] of each cell element.

mb =
  3×6 cell array
    {1×2 double}    {1×2 double}    {1×2 double}    {1×2 double}    {1×2 double}    {1×2 double}
    {1×2 double}    {1×2 double}    {1×2 double}    {1×2 double}    {1×2 double}    {1×2 double}
    {1×2 double}    {1×2 double}    {1×2 double}    {1×2 double}    {1×2 double}    {1×2 double}

For example, for the equation in data(2,3), the slope and intercept are

   mb{2,3}
ans =
        2.774       68.856

To draw a regression line on a plot use refline()

refline(mb{2,3})

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Nathan Rivard 2019-10-16

Ok one more thing and I will 100% accept it. I would like the average of the slopes and the average of the intercepts seperately as none of the slopes are negative and they are all remotely close together

Adam Danz 2019-10-16

Continuing from the mb cell array in my answer,

allSlopes = cellfun(@(x)x(1),mb); %matrix of all slopes in mb
allInt = cellfun(@(x)x(2),mb);    %matrix of all intercepts in mb
 
meanSlope = mean(allSlopes(:)); 
meanYint = mean(allInt(:)); 