Train num_labels one-vs-all logistic regression classifiers
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1 changed files with 16 additions and 14 deletions
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@ -1,17 +1,17 @@
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function [all_theta] = oneVsAll(X, y, num_labels, lambda)
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%ONEVSALL trains multiple logistic regression classifiers and returns all
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%the classifiers in a matrix all_theta, where the i-th row of all_theta
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%the classifiers in a matrix all_theta, where the i-th row of all_theta
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%corresponds to the classifier for label i
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% [all_theta] = ONEVSALL(X, y, num_labels, lambda) trains num_labels
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% logisitc regression classifiers and returns each of these classifiers
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% in a matrix all_theta, where the i-th row of all_theta corresponds
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% in a matrix all_theta, where the i-th row of all_theta corresponds
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% to the classifier for label i
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% Some useful variables
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m = size(X, 1);
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n = size(X, 2);
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% You need to return the following variables correctly
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% You need to return the following variables correctly
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all_theta = zeros(num_labels, n + 1);
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% Add ones to the X data matrix
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@ -20,11 +20,11 @@ X = [ones(m, 1) X];
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% ====================== YOUR CODE HERE ======================
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% Instructions: You should complete the following code to train num_labels
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% logistic regression classifiers with regularization
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% parameter lambda.
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% parameter lambda.
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%
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% Hint: theta(:) will return a column vector.
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%
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% Hint: You can use y == c to obtain a vector of 1's and 0's that tell use
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% Hint: You can use y == c to obtain a vector of 1's and 0's that tell use
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% whether the ground truth is true/false for this class.
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%
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% Note: For this assignment, we recommend using fmincg to optimize the cost
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@ -38,29 +38,31 @@ X = [ones(m, 1) X];
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%
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% % Set Initial theta
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% initial_theta = zeros(n + 1, 1);
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%
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%
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% % Set options for fminunc
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% options = optimset('GradObj', 'on', 'MaxIter', 50);
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%
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%
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% % Run fmincg to obtain the optimal theta
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% % This function will return theta and the cost
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% % This function will return theta and the cost
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% [theta] = ...
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% fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), ...
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% initial_theta, options);
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%
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for c = 1:num_labels
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% Train a one-vs all classifier for this class c
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initial_theta = zeros(n + 1, 1);
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options = optimset('GradObj', 'on', 'MaxIter', 50);
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[theta] = fmincg(@(t)(lrCostFunction(t, X, (y == c), lambda)),
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initial_theta, options);
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all_theta(c,:) = theta';
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end
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% =========================================================================
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end
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