Loading Data in Matlab

Hi Guys,
I'm trying to read some data into Matlab. This is what I've done so far.
>> selwoodData = zeros(53);
>> load selwoodData.txt;
Now what I would like to do is pick off each column into a column vector variable. Is there a way to do this without hard coding it. I was thinking of setting up a loop that read each of the 53 columns into different variables, but I'm not sure how I would change the variable name each time. Any help would be appreciated.
Thanks

Quote:

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Originally Posted by notPrime

Hi Guys,
I'm trying to read some data into Matlab. This is what I've done so far.
>> selwoodData = zeros(53);
>> load selwoodData.txt;
Now what I would like to do is pick off each column into a column vector variable. Is there a way to do this without hard coding it. I was thinking of setting up a loop that read each of the 53 columns into different variables, but I'm not sure how I would change the variable name each time. Any help would be appreciated.
Thanks

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I'm not at a machine that I can run Matlab on at present so can't check the syntax, but something like the following should do the trick:

Code:

---

sz=size(selwoodData)
 
for idx=1:sz(2)
  eval(['rootname',num2str(idx),'=selwoodData(:,idx)']);
end

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Thanks a lot. I have another question (Sorry, I literally started using Matlab last week). I have two m files fitness and DoIt. In DoIt, I have:
...Some code
fitness(gene2,gene3,gene4,gene5,gene6,actualYValue s,measureOfFit1)
In fitness, I have:
%This function measures the fitness of our chromosome by comparing the predict values to the
%actual values. The measure of fit is the square of the pearson correlation coefficient.

function[measure] = fitness(x1, x2, x3, x4, x5,yValues,fitnessValue)
%Here we form the matrix and carry out the linear regression calculations
firstColumn=ones(29,1);
theMatrix = [firstColumn, x1, x2, x3, x4, x5];
c=inv(transpose(theMatrix)*theMatrix)*transpose(th eMatrix);
%Now we will multiply the transpose of the c matrix by the the transpose of theMatrix to
%calculate our predicted y values. Using linear regression.
finalMatrix = transpose(transpose(c)*transpose(theMatrix));
predictedYValues = finalMatrix*yValues;
measure = linreg(predictedYValues, yValues);
fitnessValue=measure;

I'm trying to get the fitness function to reflect the changes in the fitnessValue done in the fitness function back to the DoIt function. How would I do this? Is there anything like the ampersand in C?

Quote:

---

Originally Posted by notPrime

Thanks a lot. I have another question (Sorry, I literally started using Matlab last week). I have two m files fitness and DoIt. In DoIt, I have:
...Some code
fitness(gene2,gene3,gene4,gene5,gene6,actualYValue s,measureOfFit1)
In fitness, I have:
%This function measures the fitness of our chromosome by comparing the predict values to the
%actual values. The measure of fit is the square of the pearson correlation coefficient.

function[measure] = fitness(x1, x2, x3, x4, x5,yValues,fitnessValue)
%Here we form the matrix and carry out the linear regression calculations
firstColumn=ones(29,1);
theMatrix = [firstColumn, x1, x2, x3, x4, x5];
c=inv(transpose(theMatrix)*theMatrix)*transpose(th eMatrix);
%Now we will multiply the transpose of the c matrix by the the transpose of theMatrix to
%calculate our predicted y values. Using linear regression.
finalMatrix = transpose(transpose(c)*transpose(theMatrix));
predictedYValues = finalMatrix*yValues;
measure = linreg(predictedYValues, yValues);
fitnessValue=measure;

I'm trying to get the fitness function to reflect the changes in the fitnessValue done in the fitness function back to the DoIt function. How would I do this? Is there anything like the ampersand in C?

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Try calling code:

Code:

---

fitnessvalue=fitness(  ..... )

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where the fitness function returns measure rather than modifies fitnessvalue:

Code:

---

function [measure] = fitness(x1, x2, x3, x4, x5,yValues,fitnessValue)
%Here we form the matrix and carry out the linear regression calculations
 
  firstColumn=ones(29,1);
  theMatrix = [firstColumn, x1, x2, x3, x4, x5];
  c=inv(transpose(theMatrix)*theMatrix)*transpose(theMatrix);
  %Now we will multiply the transpose of the c matrix by the the
  %transpose of theMatrix to
  %calculate our predicted y values. Using linear regression.
  finalMatrix = transpose(transpose(c)*transpose(theMatrix));
  predictedYValues = finalMatrix*yValues;
  measure = linreg(predictedYValues, yValues);

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CB