You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
40 lines
1.5 KiB
Matlab
40 lines
1.5 KiB
Matlab
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);
|
|
|
|
% 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.
|
|
%
|
|
|
|
[~, p] = max(X * all_theta', [], 2);
|
|
%disp(size(p));
|
|
|
|
% =========================================================================
|
|
|
|
|
|
end
|