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Better predict function #6

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f9aeb2c
Better predict function
ApoorvaRajBhadani May 22, 2019
ea430ee
Better code for regularized cost and gradient
ApoorvaRajBhadani May 22, 2019
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13 changes: 3 additions & 10 deletions machine-learning-ex2/ex2/costFunctionReg.m
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Expand Up @@ -17,16 +17,9 @@
% Compute the partial derivatives and set grad to the partial
% derivatives of the cost w.r.t. each parameter in theta

tempTheta = theta;
tempTheta(1) = 0;

J = (-1 / m) * sum(y.*log(sigmoid(X * theta)) + (1 - y).*log(1 - sigmoid(X * theta))) + (lambda / (2 * m))*sum(tempTheta.^2);
temp = sigmoid (X * theta);
error = temp - y;
grad = (1 / m) * (X' * error) + (lambda/m)*tempTheta;



[Jt,gt]=costFunction(theta,X,y);
J= Jt + sum((lambda/(2*m))*(theta(2:end).^2));
grad = gt + [0;(lambda/m)*theta(2:end)];
% =============================================================

end
9 changes: 1 addition & 8 deletions machine-learning-ex2/ex2/predict.m
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Expand Up @@ -8,19 +8,12 @@

% You need to return the following variables correctly
p = zeros(m, 1);
p = round(sigmoid(X * theta));
% ====================== 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
%







p = sigmoid(X*theta) > 0.5;
% =========================================================================


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