[MACHINE LEARNING] Week2 과제

+)Octave,MATLAB

1.Simple Octave/MATLAB function

-first assignment is really simple. this assignment is for teaching you how to submit the code and get a score

 you can open the warmUpExcercise.m file and write down A = eye(5)

 and submit that code

2.  Linear regression with one variable

2.1 Plotting the Data

Before starting on any task, it is often useful to understand the data by visualizing it. For this dataset, you can use a scatter plot to visualize the data, since it has only two properties to plot (profit and population). (Many other problems that you will encounter in real life are multi-dimensional and can’t be plotted on a 2-d plot.) In ex1.m, the dataset is loaded from the data file into the variables X and y:

data = load('ex1data1.txt'); % read comma separated data X = data(:, 1); y = data(:, 2); m = length(y); % number of training examples plot(x, y, 'rx', 'MarkerSize', 10); % Plot the data ylabel('Profit in $10,000s'); % Set the y−axis label xlabel('Population of City in 10,000s'); % Set the x−axis label

2.2 Gradient Descent

2.2.1 Update Equations

The objective of linear regression is to minimize the cost function

where the hypothesis hθ(x) is given by the linear model

gradient descent function

 (simultaneously update θj for all j).

2.2.2 Implementation

X = [ones(m, 1), data(:,1)]; % Add a column of ones to x theta = zeros(2, 1); % initialize fitting parameters iterations = 1500; alpha = 0.01;

2.2.3 Computing the cost J(θ)

ex1.m

if you want to submit code and check whether your code is right, write down code in here

they execute this function and give you score

%% =================== Part 3: Cost and Gradient descent =================== X = [ones(m, 1), data(:,1)]; % Add a column of ones to x theta = zeros(2, 1); % initialize fitting parameters % Some gradient descent settings iterations = 1500; alpha = 0.01; computeCost(X,y,theta)

computeCost.m

you have to write down right code in this function

and this assignment want to check you can make right code for calculating cost function

function J = computeCost(X, y, theta) %COMPUTECOST Compute cost for linear regression % J = COMPUTECOST(X, y, theta) computes the cost of using theta as the % parameter for linear regression to fit the data points in X and y m = length(y); J=0; % ====================== YOUR CODE HERE ====================== % Instructions: Compute the cost of a particular choice of theta % You should set J to the cost. J = (1/(2*m)) * (X*theta-y)' * (X*theta-y); % ========================================================================= end