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