Exercise:Softmax Regression

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(Step 2: Implement softmaxCost)
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  % M is the matrix as described in the text
  % M is the matrix as described in the text
-
  M = bsxfun(@minus, M, max(M));
+
  M = bsxfun(@minus, M, max(M, [], 1));
<tt>max(M)</tt> yields a row vector with each element giving the maximum value in that column. <tt>bsxfun</tt> (short for binary singleton expansion function) applies minus along each row of <tt>M</tt>, hence subtracting the maximum of each column from every element in the column.  
<tt>max(M)</tt> yields a row vector with each element giving the maximum value in that column. <tt>bsxfun</tt> (short for binary singleton expansion function) applies minus along each row of <tt>M</tt>, hence subtracting the maximum of each column from every element in the column.  

Revision as of 06:44, 6 May 2011

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