MachineLearning/machine-learning-ex1
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mjjo53
2017-05-28 17:17:42 +09:00
parent 992a5db5bf
commit 0705953efb
10 changed files with 34 additions and 12 deletions
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3
ex1/computeCost.m
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@@ -13,7 +13,8 @@ J = 0;
% Instructions: Compute the cost of a particular choice of theta % Instructions: Compute the cost of a particular choice of theta
% You should set J to the cost. % You should set J to the cost.
hx = X*theta;
J = sum((hx - y).^2)/(2*m);

3
ex1/computeCostMulti.m
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@@ -14,7 +14,8 @@ J = 0;
% You should set J to the cost. % You should set J to the cost.
hx = X*theta;
J = sum((hx - y).^2)/(2*m);
% ========================================================================= % =========================================================================

4
ex1/ex1_multi.m
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@@ -105,7 +105,7 @@ fprintf('\n');
% Recall that the first column of X is all-ones. Thus, it does % Recall that the first column of X is all-ones. Thus, it does
% not need to be normalized. % not need to be normalized.
price = 0; % You should change this price = 0; % You should change this
price = [1, 1650, 3]*theta;
% ============================================================ % ============================================================
@@ -150,7 +150,7 @@ fprintf('\n');
% Estimate the price of a 1650 sq-ft, 3 br house % Estimate the price of a 1650 sq-ft, 3 br house
% ====================== YOUR CODE HERE ====================== % ====================== YOUR CODE HERE ======================
price = 0; % You should change this price = 0; % You should change this
price = [1, 1650, 3]*theta;
% ============================================================ % ============================================================

7
ex1/featureNormalize.m
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@@ -26,9 +26,10 @@ sigma = zeros(1, size(X, 2));
% Hint: You might find the 'mean' and 'std' functions useful. % Hint: You might find the 'mean' and 'std' functions useful.
% %
mu = mean(X);
X_norm .-= mu;
sigma = std(X);
X_norm ./= sigma;

3
ex1/gradientDescent.m
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@@ -18,7 +18,8 @@ for iter = 1:num_iters
% %
hx = X*theta;
theta -= (alpha*sum((hx-y).*X)/m)';

3
ex1/gradientDescentMulti.m
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@@ -20,7 +20,8 @@ for iter = 1:num_iters
hx = X*theta;
theta -= (alpha*sum((hx-y).*X)/m)';

2
ex1/normalEqn.m
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@@ -12,7 +12,7 @@ theta = zeros(size(X, 2), 1);
% ---------------------- Sample Solution ---------------------- % ---------------------- Sample Solution ----------------------
theta = pinv(X'*X)*X'*y;
% ------------------------------------------------------------- % -------------------------------------------------------------

4
ex1/plotData.m
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@@ -17,7 +17,9 @@ figure; % open a new figure window
% markers larger by using plot(..., 'rx', 'MarkerSize', 10); % markers larger by using plot(..., 'rx', 'MarkerSize', 10);
plot(x, y, 'rx', 'MarkerSize', 10);
ylabel('Profit in $10,000s');
xlabel('Population of City in 10,000s');
% ============================================================ % ============================================================

15
ex1/token.mat Normal file
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@@ -0,0 +1,15 @@
# Created by Octave 4.2.1, Sun May 28 17:11:16 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
9Dxrb898sAaadc8j

2
ex1/warmUpExercise.m
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@@ -11,7 +11,7 @@ A = [];
A = eye(5);