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This is my solution to costFunctionReg.m function in Programming assignment 2 from the famous Machine Learning course by Andrew Ng.
Github: https://github.com/AladdinPerzon/Courses/tree/master/MOOCS/Coursera-Machine-Learning
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costFunctionReg.m – searchcode
/ex2/ex2/costFunctionReg.m. https://bitbucket.org/erogol/machine-learning-coursera-assignment-codes. Objective C | 32 lines | 23 code | 9 blank | 0 comment …
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Date Published: 7/21/2022
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Top 9 Costfunctionreg M Coursera Trust The Answer
Summary of article content: Articles about cyh/coursera_machine_learning_ex: coursera machine learning exercise solution – Gogs costFunctionReg.
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coursera machine learning exercise solution – Gogs
costFunctionReg.m 1.1 KB. Permalink History Raw · function [J, grad] = costFunctionReg(theta, X, y, lambda) · %COSTFUNCTIONREG Compute cost and gradient for …
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Date Published: 6/10/2021
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Submit error in Logistic Regression – Support Center
FileName: C:\Users\naman\OneDrive\Desktop\machine-learning-ex2\ex2\costFunctionReg.m. LineNumber: 21. I am sharing my costFunctionReg file code below:.
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Date Published: 12/22/2022
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coursera-ml-007-exercises/costFunctionReg.m at …
function [J, grad] = costFunctionReg(theta, X, y, lambda). %COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization.
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Date Published: 11/25/2022
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Coursera machine learning course notes (3) – Katastros
Coursera machine learning course notes (3) … 5.Regularized Logistic Regression Cost. Completed at the same time as 6, all in the costFunctionReg.m file …
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Date Published: 4/26/2021
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Coursera: Machine Learning (Week 3) [Assignment Solution]
[*] costFunctionReg.m – Regularized Logistic Regression Cost. * indicates files you will need to complete. plotData.m :.Source: solutionproviderdaily.blogspot.com
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주제에 대한 기사 평가 costfunctionreg m coursera
- Author: Aladdin Persson
- Views: 조회수 5,527회
- Likes: 좋아요 64개
- Date Published: 2019. 2. 13.
- Video Url link: https://www.youtube.com/watch?v=GMmUg5OCZk4
costFunctionReg.m
PageRenderTime 20ms CodeModel.GetById 11ms app.highlight 7ms RepoModel.GetById 1ms app.codeStats 0ms
/ex2/ex2/costFunctionReg.m
Objective C | 32 lines | 23 code | 9 blank | 0 comment | 3 complexity | 4ebc7f07266cf1e503a8e083dda293db MD5 | raw file
Top 9 Costfunctionreg M Coursera Trust The Answer
costFunctionReg.m – Programming Assignment 2 Machine Learning
costFunctionReg.m – Programming Assignment 2 Machine Learning
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Katastros
1Sigmoid Function
2Logistic Regression Cost
3Logistic Regression Gradient
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5Regularized Logistic Regression Cost
6Regularized Logistic Regression Gradient
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function [J, grad] = costFunctionReg(theta, X, y, lambda) %COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization % J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using % theta as the parameter for regularized logistic regression and the % gradient of the cost w.r.t. to the parameters. % Initialize some useful values m = length(y); % number of training examples % You need to return the following variables correctly z = hypothesis(theta, X); t = lambda*(sum(theta .^ 2)-theta(1)^2)/2/m; J = mean(- y .* log(z) + (y – 1) .* log(1 – z)) + t; grad = mean((z – y) .* X)’ + lambda /m * theta; grad(1) = grad(1) – lambda /m * theta(1); % ====================== YOUR CODE HERE ====================== % Instructions: Compute the cost of a particular choice of theta. % You should set J to the cost. % Compute the partial derivatives and set grad to the partial % derivatives of the cost w.r.t. each parameter in theta % ============================================================= end
Coursera machine learning course notes (3)
Record the answers to the assignment
1.Sigmoid Function
sigmoid.m file:
function g = sigmoid (z) %SIGMOID Compute sigmoid function % g = SIGMOID(z) computes the sigmoid of z. % You need to return the following variables correctly g = zeros ( size (z)); % ====================== YOUR CODE HERE ====================== % Instructions: Compute the sigmoid of each value of z (z can be a matrix, % vector or scalar). [m,n] = size (z); for i = 1 :m for j = 1 :n g( i , j ) = 1 /( 1 + exp (-z( i , j ))); end end % ============================================================= end
2.Logistic Regression Cost
Completed at the same time as 3, all in the costFunction.m file
3.Logistic Regression Gradient
costFunction.m file:
function [J, grad] = costFunction(theta, X, y) %COSTFUNCTION Compute cost and gradient for logistic regression % J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the % parameter for logistic regression and the gradient of the cost % w.r.t. to the parameters. % Initialize some useful values m = length (y); % number of training examples % You need to return the following variables correctly J = 0 ; grad = zeros(size(theta)); % ====================== YOUR CODE HERE ====================== % Instructions: Compute the cost of a particular choice of theta. % You should set J to the cost. % Compute the partial derivatives and set grad to the partial % derivatives of the cost w.r.t. each parameter in theta % % Note: grad should have the same dimensions as theta % J = ( 1 /m) * (-y’ * log (sigmoid(X * theta)) – ( 1 -y)’ * log ( 1 – sigmoid(X * theta))); for j = 1 : length (theta) grad(j) = ( 1 /m) * X(:,j)’ * (sigmoid(X * theta) – y); end % ============================================================= end
4.Predict
predict.m file:
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
5.Regularized Logistic Regression Cost
Completed at the same time as 6, all in the costFunctionReg.m file
6.Regularized Logistic Regression Gradient
costFunctionReg.m file:
cyh/coursera_machine_learning_ex
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
function [J, grad] = costFunctionReg(theta, X, y, lambda) %COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization % J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using % theta as the parameter for regularized logistic regression and the % gradient of the cost w.r.t. to the parameters. % Initialize some useful values m = length(y); % number of training examples % You need to return the following variables correctly z = hypothesis(theta, X); t = lambda*(sum(theta .^ 2)-theta(1)^2)/2/m; J = mean(- y .* log(z) + (y – 1) .* log(1 – z)) + t; grad = mean((z – y) .* X)’ + lambda /m * theta; grad(1) = grad(1) – lambda /m * theta(1); % ====================== YOUR CODE HERE ====================== % Instructions: Compute the cost of a particular choice of theta. % You should set J to the cost. % Compute the partial derivatives and set grad to the partial % derivatives of the cost w.r.t. each parameter in theta % ============================================================= end
coursera-ml-007-exercises
exercises from the coursera machine learning course
This repo is archived. You can view files and clone it, but cannot push or open issues/pull-requests.
Coursera machine learning course notes (3)
Coursera machine learning course notes (3)
Record the answers to the assignment
1.Sigmoid Function
sigmoid.m file:
function g = sigmoid (z) %SIGMOID Compute sigmoid function % g = SIGMOID(z) computes the sigmoid of z. % You need to return the following variables correctly g = zeros ( size (z)); % ====================== YOUR CODE HERE ====================== % Instructions: Compute the sigmoid of each value of z (z can be a matrix, % vector or scalar). [m,n] = size (z); for i = 1 :m for j = 1 :n g( i , j ) = 1 /( 1 + exp (-z( i , j ))); end end % ============================================================= end
2.Logistic Regression Cost
Completed at the same time as 3, all in the costFunction.m file
3.Logistic Regression Gradient
costFunction.m file:
function [J, grad] = costFunction(theta, X, y) %COSTFUNCTION Compute cost and gradient for logistic regression % J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the % parameter for logistic regression and the gradient of the cost % w.r.t. to the parameters. % Initialize some useful values m = length (y); % number of training examples % You need to return the following variables correctly J = 0 ; grad = zeros(size(theta)); % ====================== YOUR CODE HERE ====================== % Instructions: Compute the cost of a particular choice of theta. % You should set J to the cost. % Compute the partial derivatives and set grad to the partial % derivatives of the cost w.r.t. each parameter in theta % % Note: grad should have the same dimensions as theta % J = ( 1 /m) * (-y’ * log (sigmoid(X * theta)) – ( 1 -y)’ * log ( 1 – sigmoid(X * theta))); for j = 1 : length (theta) grad(j) = ( 1 /m) * X(:,j)’ * (sigmoid(X * theta) – y); end % ============================================================= end
4.Predict
predict.m file:
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
5.Regularized Logistic Regression Cost
Completed at the same time as 6, all in the costFunctionReg.m file
6.Regularized Logistic Regression Gradient
costFunctionReg.m file:
Coursera: Machine Learning (Week 3) [Assignment Solution] – Andrew NG
function
plotData
(
)
%PLOTDATA Plots the data points X and y into a new figure
% PLOTDATA(x,y) plots the data points with + for the positive examples
% and o for the negative examples. X is assumed to be a Mx2 matrix.
% ====================== YOUR CODE HERE ======================
% Instructions: Plot the positive and negative examples on a
% 2D plot, using the option ‘k+’ for the positive
% examples and ‘ko’ for the negative examples.
%
%Seperating positive and negative results
pos
=
find
(
y
==
1
);
%index of positive results
neg
=
find
(
y
==
0
);
%index of negative results
% Create New Figure
figure
;
%Plotting Positive Results on
% X_axis: Exam1 Score = X(pos,1)
% Y_axis: Exam2 Score = X(pos,2)
plot
(
X
(
pos
,
1
),
X
(
pos
,
2
),
‘g+’
);
%To keep above plotted graph as it is.
hold
on
;
%Plotting Negative Results on
% X_axis: Exam1 Score = X(neg,1)
% Y_axis: Exam2 Score = X(neg,2)
plot
(
X
(
neg
,
1
),
X
(
neg
,
2
),
‘ro’
);
% =========================================================================
hold
off
;
end
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