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Finishes Week 6

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julien Lengrand-Lambert
2015-12-12 11:15:26 +01:00
parent 78ede21908
commit cfcd8b02b0
4 changed files with 12 additions and 12 deletions
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4
machine-learning-ex5/ex5/ex5.m
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@@ -123,7 +123,7 @@ for i = 1:m
end end
fprintf('Program paused. Press enter to continue.\n'); fprintf('Program paused. Press enter to continue.\n');
pause; % pause;
%% =========== Part 6: Feature Mapping for Polynomial Regression ============= %% =========== Part 6: Feature Mapping for Polynomial Regression =============
% One solution to this is to use polynomial regression. You should now % One solution to this is to use polynomial regression. You should now
@@ -153,7 +153,7 @@ fprintf('Normalized Training Example 1:\n');
fprintf(' %f \n', X_poly(1, :)); fprintf(' %f \n', X_poly(1, :));
fprintf('\nProgram paused. Press enter to continue.\n'); fprintf('\nProgram paused. Press enter to continue.\n');
pause; % pause;

10
machine-learning-ex5/ex5/polyFeatures.m
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@@ -15,11 +15,9 @@ X_poly = zeros(numel(X), p);
% %
% %
for i = 1:p
X_poly(:, i) = X.^i;
end
% ========================================================================= % =========================================================================
end
end

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machine-learning-ex5/ex5/token.mat
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10
machine-learning-ex5/ex5/validationCurve.m
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@@ -41,10 +41,12 @@ error_val = zeros(length(lambda_vec), 1);
for i = 1:length(lambda_vec)
lambda = lambda_vec(i);
[theta] = trainLinearReg(X, y, lambda);
error_train(i) = linearRegCostFunction(X, y, theta, 0);
error_val(i) = linearRegCostFunction(Xval, yval, theta, 0);
end