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- function p = predictOneVsAll(all_theta, X)
- %PREDICT Predict the label for a trained one-vs-all classifier. The labels
- %are in the range 1..K, where K = size(all_theta, 1).
- % p = PREDICTONEVSALL(all_theta, X) will return a vector of predictions
- % for each example in the matrix X. Note that X contains the examples in
- % rows. all_theta is a matrix where the i-th row is a trained logistic
- % regression theta vector for the i-th class. You should set p to a vector
- % of values from 1..K (e.g., p = [1; 3; 1; 2] predicts classes 1, 3, 1, 2
- % for 4 examples)
- m = size(X, 1);
- num_labels = size(all_theta, 1);
- % You need to return the following variables correctly
- p = zeros(size(X, 1), 1);
- v = zeros(size(X, 1), 1);
- % Add ones to the X data matrix
- X = [ones(m, 1) X];
- % ====================== YOUR CODE HERE ======================
- % Instructions: Complete the following code to make predictions using
- % your learned logistic regression parameters (one-vs-all).
- % You should set p to a vector of predictions (from 1 to
- % num_labels).
- %
- % Hint: This code can be done all vectorized using the max function.
- % In particular, the max function can also return the index of the
- % max element, for more information see 'help max'. If your examples
- % are in rows, then, you can use max(A, [], 2) to obtain the max
- % for each row.
- %
- % refer to week 3 reading: multiclass classification: 1 vs all
- % at the last line it mentions picking the class that maximize h function
- XT = X * all_theta';
- [v,p] = max(XT, [], 2);
- %size(p)
- r = [v,p];
- unique(p);
- % =========================================================================
- end
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