MachineLearning/machine-learning-ex2
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mjjo53
2017-05-28 06:50:45 +09:00
parent b4c0085bfb
commit fd8432ccbf
7 changed files with 34 additions and 11 deletions
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4
ex2/costFunction.m
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@@ -20,8 +20,10 @@ grad = zeros(size(theta));
% Note: grad should have the same dimensions as theta % Note: grad should have the same dimensions as theta
% %
hx = sigmoid(X*theta);
J = sum(-y.*log(hx) - (1.-y).*log(1.-hx))/m;
grad = sum((hx-y).*X)/m;

6
ex2/costFunctionReg.m
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@@ -18,9 +18,9 @@ grad = zeros(size(theta));
% derivatives of the cost w.r.t. each parameter in theta % derivatives of the cost w.r.t. each parameter in theta
hx = sigmoid(X*theta);
J = sum(-y.*log(hx)-(1.-y).*log(1.-hx))/m + lambda*sum([0; theta(2:end)].^2)/(2*m);
grad = (sum((hx-y).*X)./m)' + lambda*[0; theta(2:end)]/m;
% ============================================================= % =============================================================

6
ex2/ex2_reg.m
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@@ -75,7 +75,7 @@ fprintf('Expected gradients (approx) - first five values only:\n');
fprintf(' 0.0085\n 0.0188\n 0.0001\n 0.0503\n 0.0115\n'); fprintf(' 0.0085\n 0.0188\n 0.0001\n 0.0503\n 0.0115\n');
fprintf('\nProgram paused. Press enter to continue.\n'); fprintf('\nProgram paused. Press enter to continue.\n');
pause; %pause;
% Compute and display cost and gradient % Compute and display cost and gradient
% with all-ones theta and lambda = 10 % with all-ones theta and lambda = 10
@@ -90,7 +90,7 @@ fprintf('Expected gradients (approx) - first five values only:\n');
fprintf(' 0.3460\n 0.1614\n 0.1948\n 0.2269\n 0.0922\n'); fprintf(' 0.3460\n 0.1614\n 0.1948\n 0.2269\n 0.0922\n');
fprintf('\nProgram paused. Press enter to continue.\n'); fprintf('\nProgram paused. Press enter to continue.\n');
pause; %pause;
%% ============= Part 2: Regularization and Accuracies ============= %% ============= Part 2: Regularization and Accuracies =============
% Optional Exercise: % Optional Exercise:
@@ -107,7 +107,7 @@ pause;
initial_theta = zeros(size(X, 2), 1); initial_theta = zeros(size(X, 2), 1);
% Set regularization parameter lambda to 1 (you should vary this) % Set regularization parameter lambda to 1 (you should vary this)
lambda = 1; lambda = 100;
% Set Options % Set Options
options = optimset('GradObj', 'on', 'MaxIter', 400); options = optimset('GradObj', 'on', 'MaxIter', 400);

9
ex2/plotData.m
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@@ -12,10 +12,15 @@ figure; hold on;
% examples and 'ko' for the negative examples. % examples and 'ko' for the negative examples.
% %
pos = find(y==1);
neg = find(y == 0);
plot(X(pos, 1), X(pos, 2), 'k+','LineWidth', 2, 'MarkerSize', 7);
plot(X(neg, 1), X(neg, 2), 'ko', 'MarkerFaceColor', 'y', 'MarkerSize', 7);
ylabel('Exam 2 score');
xlabel('Exam 1 score');
legend('Admitted', 'Not admitted');

3
ex2/predict.m
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@@ -17,7 +17,8 @@ p = zeros(m, 1);
hx = X*theta;
p = hx >= 0;

2
ex2/sigmoid.m
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@@ -10,7 +10,7 @@ g = zeros(size(z));
% vector or scalar). % vector or scalar).
g = 1./(1+exp(-z));
% ============================================================= % =============================================================

15
ex2/token.mat Normal file
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@@ -0,0 +1,15 @@
# Created by Octave 4.2.1, Wed May 10 12:15:18 2017 GMT <unknown@unknown>
# name: email
# type: sq_string
# elements: 1
# length: 16
mjjo53@gmail.com
# name: token
# type: sq_string
# elements: 1
# length: 16
fZMMhxjQUsSZ1osy