Question

I have to implement a single layer neural network or perceptron.For this, I have 2 files data sets , one for the input and one for the output.I have to do this in matlab without using neural toolbox.The format of 2 files is given below.

 In:
    0.832 64.643
    0.818 78.843
    1.776 45.049
    0.597 88.302
    1.412 63.458


Out:
0 0 1
0 0 1
0 1 0
0 0 1
0 0 1

The target output is “1 for a particular class that the corresponding input belongs to and “0 for the remaining 2 outputs.

I tried to do this, But it is not working for me.

load in.data
load out.data
x = in(:1);
y = in(:2);

learning rate = 0.2;
max_iteration = 50;

function result = calculateOutput(weights,x, y)
s = x*(weights(1) +weight(2) +weight(3));
if s>=0
 result = 1
else:
 result = -1
end
end

Count = length(x);
weights[0] = rand();
weights[1] = rand();
weights[2] = rand();

iter = 0;
do {
  iter++;
  globalerror = 0;
  for(p=0; p<count;p++){
    output = calculateoutput(weights,x[p],y[p]);
    localerror = output[p] - output
    weights[0]+= learningrate *localerror*x[p];
    weights[1]+= learningrate *localerror*y[p];
    weights[2]+= learningrate *localerror;
    globalerror +=(localerror*localerror);
   }
}while(globalerror != 0 && iter <= max_iteration);

Where is the mistake in this algorithm??

I am referring the example given in the link below:-

Perceptron learning algorithm not converging to 0

Answer 1

Here’s a list of what I see wrong:

deep breath

• The indexing syntax (:1) is incorrect. Perhaps you mean (:,1) as in “all rows of column 1”.
• There is no such thing as a do…while loop in MATLAB. Only FOR and WHILE loops. Also, your for loop is defined wrong. MATLAB has a different syntax for that.
• There are no ++ or += operators in MATLAB.
• The “not equal” operator in MATLAB is ~=, not !=.
• Indexing of vectors and matrices needs to be done with parentheses (), not square brackets [].
• Is all of this inside a function or a script? You can not define functions, namely calculateOutput, in a script. You would have to put that in its own m-file calculateOutput.m. If all of the code is actually inside a larger function, then calculateOutput is a nested function and should work fine (assuming you have ended the larger enclosing function with an end).
• You have a number of apparent typos for variable names:
  • weight vs. weights (as per phoffer’s answer)
  • Count vs. count (MATLAB is case-sensitive)
  • calculateOutput vs. calculateoutput (again, case-sensitivity)
  • learning rate vs. learningrate (variables can’t have spaces in them)

heavy exhale 😉

In short, it needs quite a bit of work.