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POPerceptron training in Matlab
 

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  1. POPerceptron training in Matlab
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    <p>I am trying to create a simple perceptron training function in MATLAB. I want to return the weights when no errors are found.</p> <p>Here is the data I want to classify.</p> <pre><code>d = rand(10,2); figure labels = ones(10,1); diff = d(:,1) + d(:,2) - 1; labels( diff + 1 &gt;= 1) = 2; pathWidth = 0.05; labels( abs(diff) &lt; pathWidth) = []; d(abs(diff) &lt; pathWidth,:) = []; plot(d(labels == 1,1),d(labels == 1,2),'k.','MarkerSize',10) plot(d(labels == 2,1),d(labels == 2,2),'r.','MarkerSize',10) </code></pre> <p>It produces a labelled data set, where the division between the two classes (red, black) are more visible if you increase the number of points for d.</p> <p>For my perceptron function I pass the data (d) and labels. I have 3 inputs, the x values, y values and bias which is one. Each input has a random weight between 0 and 1 assigned. Note that the dataset d I have named Z in the perceptron function. I did use a sigmoid activation function but that would run once through the while loop and always return true after that, the sigmoid function also gave me values of either inf or 1. Below I am using just a threshold activation but it seems to continually loop and not returning my weights. I think the problem may lie in the if-statement below</p> <pre><code>if(v &gt;= 0 &amp;&amp; labels(i) == 1 || v &lt; 0 &amp;&amp; labels(i) == 2) </code></pre> <p>Perceptron function:</p> <pre><code>function perceptron(data,labels) sizea = numel(data(:,1)); weights = rand(sizea,3); Z = data(:,:) eta = 0.5; errors = 1; count = 0; while errors &gt; 0 errors = 0; v = sum((1*weights(1,1)) + (Z(:,1)*weights(1,2)) + (Z(:,2)*weights(1,3))); if v &gt;= 1 v = 1; else v = 0; end count = count + 1 for i = 1:sizea % for each object in dataset if(v == 1 &amp;&amp; labels(i) == 1 || v == 0 &amp;&amp; labels(i) == 2) errors = 1; weights(1,1) = weights(1,1) - (2*v-1) * eta * 1; weights(1,2) = weights(1,2) - (2*v-1) * eta * Z(i,1); weights(1,3) = weights(1,3) - (2*v-1) * eta * Z(i,2); v = sum((1*weights(1,1)) + (Z(:,1)*weights(1,2)) + (Z(:,2)*weights(1,3))); if v &gt;= 1 v = 1; else v = 0; end end end end </code></pre>
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    1. USTom smith
    1. USTom smith
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    1. COThere are multiple issues when someone is trying to execute the code you provided. Minor: Add a 'hold on' statement between the two plots. Major : I would recommend debugging the second part of the code you provided (the actual perceptron algorithm), so that the variables : weights, Z, eta, sizea etc. are correctly initialized / defined. Cheers
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      1. POPerceptron training in Matlab
      1. USChris
 

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