深度学习 Deep Learning UFLDL 最新Tutorial 学习笔记 3：Vectorization

function [f,g] = linear_regression(theta, X,y)
  %
  % Arguments:
  %   theta - A vector containing the parameter values to optimize.
  %   X - The examples stored in a matrix.
  %       X(i,j) is the i‘th coordinate of the j‘th example.
  %   y - The target value for each example.  y(j) is the target for example j.
  %
  
  m=size(X,2);
  n=size(X,1);

  f=0;
  g=zeros(size(theta));

  %
  % TODO:  Compute the linear regression objective by looping over the examples in X.
  %        Store the objective function value in ‘f‘.
  %
  % TODO:  Compute the gradient of the objective with respect to theta by looping over
  %        the examples in X and adding up the gradient for each example.  Store the
  %        computed gradient in ‘g‘.
  
%%% YOUR CODE HERE %%%

% Step 1 : Compute f cost function
for i = 1:m
    f = f + (theta‘ * X(:,i) - y(i))^2;
end

f = 1/2*f;

% Step 2: Compute gradient 

for j = 1:n
    for i = 1:m
        g(j) = g(j) + X(j,i)*(theta‘ * X(:,i) - y(i));
    end
    
end

function [f,g] = linear_regression_vec(theta, X,y)
  %
  % Arguments:
  %   theta - A vector containing the parameter values to optimize.
  %   X - The examples stored in a matrix.
  %       X(i,j) is the i‘th coordinate of the j‘th example.
  %   y - The target value for each example.  y(j) is the target for example j.
  %
  m=size(X,2);
  
  % initialize objective value and gradient.
  f = 0;
  g = zeros(size(theta));

  %
  % TODO:  Compute the linear regression objective function and gradient 
  %        using vectorized code.  (It will be just a few lines of code!)
  %        Store the objective function value in ‘f‘, and the gradient in ‘g‘.
  %
%%% YOUR CODE HERE %%%
  f = 1/2*sum((theta‘*X - y).^2);
  
  g = X*(theta‘*X - y)‘;

function [f,g] = logistic_regression_vec(theta, X,y)
  %
  % Arguments:
  %   theta - A column vector containing the parameter values to optimize.
  %   X - The examples stored in a matrix.  
  %       X(i,j) is the i‘th coordinate of the j‘th example.
  %   y - The label for each example.  y(j) is the j‘th example‘s label.
  %
  m=size(X,2);
  
  % initialize objective value and gradient.
  f = 0;
  g = zeros(size(theta));
  

  %
  % TODO:  Compute the logistic regression objective function and gradient 
  %        using vectorized code.  (It will be just a few lines of code!)
  %        Store the objective function value in ‘f‘, and the gradient in ‘g‘.
  %
%%% YOUR CODE HERE %%%
f = -sum(y.*log(sigmoid(theta‘*X)) + (1-y).*log(1 - sigmoid(theta‘*X)));
g = X*(sigmoid(theta‘*X) - y)‘;