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1つまたはすべての回帰

私は https://github.com/jcgillespie/Coursera-Machine-Learning/tree/master/ex で見つけた、機械学習におけるAndrew Ngのコースの例をレビューしています。この例では、ロジスティック回帰と1対すべての分類を扱います。この機能に疑問があります：

function [all_theta] = oneVsAll(X, y, num_labels, lambda)
%ONEVSALL trains multiple logistic regression classifiers and returns all
%the classifiers in a matrix all_theta, where the i-th row of all_theta 
%corresponds to the classifier for label i
%   [all_theta] = ONEVSALL(X, y, num_labels, lambda) trains num_labels
%   logisitc regression classifiers and returns each of these classifiers
%   in a matrix all_theta, where the i-th row of all_theta corresponds 
%   to the classifier for label i

% Some useful variables
m = size(X, 1);
n = size(X, 2);

% You need to return the following variables correctly 
all_theta = zeros(num_labels, n + 1);

% Add ones to the X data matrix
X = [ones(m, 1) X];

% ====================== YOUR CODE HERE ======================
% Instructions: You should complete the following code to train num_labels
%               logistic regression classifiers with regularization
%               parameter lambda. 
%
% Hint: theta(:) will return a column vector.
%
% Hint: You can use y == c to obtain a vector of 1's and 0's that tell use 
%       whether the ground truth is true/false for this class.
%
% Note: For this assignment, we recommend using fmincg to optimize the cost
%       function. It is okay to use a for-loop (for c = 1:num_labels) to
%       loop over the different classes.
%
%       fmincg works similarly to fminunc, but is more efficient when we
%       are dealing with large number of parameters.
%
% Example Code for fmincg:
%
%     % Set Initial theta
%     initial_theta = zeros(n + 1, 1);
%     
%     % Set options for fminunc
%     options = optimset('GradObj', 'on', 'MaxIter', 50);
% 
%     % Run fmincg to obtain the optimal theta
%     % This function will return theta and the cost 
%     [theta] = ...
%         fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), ...
%                 initial_theta, options);
%

initial_theta = zeros(n + 1, 1);

options = optimset('GradObj', 'on', 'MaxIter', 50);

for i = 1:num_labels

    c = i * ones(size(y));
    fprintf('valores')
    [theta] = fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), initial_theta, options);
    all_theta(i,:) = theta;

end


% =========================================================================


end

LrCostFunctionがパラメータとしてtheta、X、y、lambdaを取ることは知っていますが、tの値がどこから来ているのかはわかりません上記で投稿したコード。特にこの部分では：

[theta] = fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), initial_theta, options);

何か助け？

matlablogistic-regression
14
Little

fmincgは、目的関数のハンドルを最初の引数として受け取ります。この場合、これはlrCostFunctionへのハンドルです。

fmincg.mに入ると、次の行が見つかります。

argstr = ['feval(f, X'];                      % compose string used to call function

%---Code will not enter the following loop---%
for i = 1:(nargin - 3) %this will go from 1 to 0, thus the loop is skipped
   argstr = [argstr, ',P', int2str(i)];
end
% following will be executed
argstr = [argstr, ')'];

上記のコードスニペットの最後に、結果は、

argstr=feval(f,X');

少し先に進んだら、

[f1 df1] = eval(argstr);                      % get function value and gradient

したがって、関数ハンドルfは引数X'で実行されます。したがって、t=X'も意味があります。最初のthetaは収束して、ロジスティック回帰の最終的なパラメーターベクトルを提供します。

8
Parag S. Chandakkar

実際に代用できます。

for i=1 : num_labels

    [theta]= fmincg (@(t)(lrCostFunction(t, X, (y == i), lambda)),initial_theta, options);

all_theta(i,:)=theta;
5
tanay

これを試して

for i = 1:num_labels,
    [all_theta(i,:)] = fmincg (@(t)(lrCostFunction(t, X, (y == i), lambda)), initial_theta, options);
end;

最初にall_thetaを初期化する必要もありません

3
nitya