求得θ值后用模型来预测 / 计算模型的精度

 ex2.m部分程序

%% ============== Part 4: Predict and Accuracies ==============

% After learning the parameters, you'll like to use it to predict the outcomes

% on unseen data. In this part, you will use the logistic regression model

% to predict the probability that a student with score 45 on exam 1 and

% score 85 on exam 2 will be admitted.

%

% Furthermore, you will compute the training and test set accuracies of

% our model.

%

% Your task is to complete the code in predict.m

% Predict probability for a student with score 45 on exam 1

% and score 85 on exam 2

prob = sigmoid([1 45 85] * theta);

fprintf(['For a student with scores 45 and 85, we predict an admission ' ...

'probability of %f\n\n'], prob);

% Compute accuracy on our training set

p = predict(theta, X);

fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100);   %若p==y,则返回1否则返回0;然后对这些0，1求平均值

fprintf('\nProgram paused. Press enter to continue.\n');

pause;  

 

predict.m

function p = predict(theta, X)

%PREDICT Predict whether the label is 0 or 1 using learned logistic

%regression parameters theta

% p = PREDICT(theta, X) computes the predictions for X using a

% threshold at 0.5 (i.e., if sigmoid(theta'*x) >= 0.5, predict 1)

m = size(X, 1); % Number of training examples

% You need to return the following variables correctly

p = zeros(m, 1);

% ====================== YOUR CODE HERE ======================

% Instructions: Complete the following code to make predictions using

% your learned logistic regression parameters.

% You should set p to a vector of 0's and 1's

%

for i=1:m

    if sigmoid(X(i,:) * theta) >=0.5

        p(i) = 1;

    else

        p(i) = 0;

    end

end

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

end