df: r6 octave
This commit is contained in:
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ds/25-1/r/6/ex1.pdf
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ds/25-1/r/6/ex1.pdf
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ds/25-1/r/6/mlclass-ex1/computeCost.m
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ds/25-1/r/6/mlclass-ex1/computeCost.m
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function J = computeCost(X, y, theta)
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%COMPUTECOST Compute cost for linear regression
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% J = COMPUTECOST(X, y, theta) computes the cost of using theta as the
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% parameter for linear regression to fit the data points in X and y
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% Initialize some useful values
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m = length(y); % number of training examples
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% You need to return the following variables correctly
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J = 0;
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% ====================== YOUR CODE HERE ======================
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% Instructions: Compute the cost of a particular choice of theta
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% You should set J to the cost.
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% X: (m, 2)
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% y: (m, 1)
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% theta: (2, 1)
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h = X * theta;
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dif = h - y;
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sqdif = dif .^ 2;
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J = 1 / (2 * m) * sum(sqdif);
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% =========================================================================
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end
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ds/25-1/r/6/mlclass-ex1/computeCostMulti.m
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ds/25-1/r/6/mlclass-ex1/computeCostMulti.m
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function J = computeCostMulti(X, y, theta)
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%COMPUTECOSTMULTI Compute cost for linear regression with multiple variables
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% J = COMPUTECOSTMULTI(X, y, theta) computes the cost of using theta as the
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% parameter for linear regression to fit the data points in X and y
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% Initialize some useful values
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m = length(y); % number of training examples
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% You need to return the following variables correctly
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J = 0;
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% ====================== YOUR CODE HERE ======================
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% Instructions: Compute the cost of a particular choice of theta
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% You should set J to the cost.
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h = X * theta;
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dif = h - y;
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sqdif = dif .^ 2;
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J = 1 / (2 * m) * sum(sqdif);
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% =========================================================================
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end
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122
ds/25-1/r/6/mlclass-ex1/ex1.m
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ds/25-1/r/6/mlclass-ex1/ex1.m
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%% Machine Learning Online Class - Exercise 1: Linear Regression
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% Instructions
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% ------------
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%
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% This file contains code that helps you get started on the
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% linear exercise. You will need to complete the following functions
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% in this exericse:
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%
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% warmUpExercise.m
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% plotData.m
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% gradientDescent.m
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% computeCost.m
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% gradientDescentMulti.m
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% computeCostMulti.m
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% featureNormalize.m
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% normalEqn.m
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%
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% For this exercise, you will not need to change any code in this file,
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% or any other files other than those mentioned above.
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%
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% x refers to the population size in 10,000s
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% y refers to the profit in $10,000s
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%
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%% Initialization
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clear all; close all; clc
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%% ==================== Part 1: Basic Function ====================
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% Complete warmUpExercise.m
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fprintf('Running warmUpExercise ... \n');
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fprintf('5x5 Identity Matrix: \n');
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warmUpExercise()
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ======================= Part 2: Plotting =======================
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fprintf('Plotting Data ...\n')
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data = load('ex1data1.txt');
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X = data(:, 1); y = data(:, 2);
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m = length(y); % number of training examples
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% Plot Data
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% Note: You have to complete the code in plotData.m
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plotData(X, y);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% =================== Part 3: Gradient descent ===================
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fprintf('Running Gradient Descent ...\n')
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X = [ones(m, 1), data(:,1)]; % Add a column of ones to x
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theta = zeros(2, 1); % initialize fitting parameters
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% Some gradient descent settings
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iterations = 1500;
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alpha = 0.01;
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% compute and display initial cost
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computeCost(X, y, theta)
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% run gradient descent
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theta = gradientDescent(X, y, theta, alpha, iterations);
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% print theta to screen
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fprintf('Theta found by gradient descent: ');
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fprintf('%f %f \n', theta(1), theta(2));
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% Plot the linear fit
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hold on; % keep previous plot visible
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plot(X(:,2), X*theta, '-')
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legend('Training data', 'Linear regression')
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hold off % don't overlay any more plots on this figure
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% Predict values for population sizes of 35,000 and 70,000
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predict1 = [1, 3.5] *theta;
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fprintf('For population = 35,000, we predict a profit of %f\n',...
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predict1*10000);
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predict2 = [1, 7] * theta;
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fprintf('For population = 70,000, we predict a profit of %f\n',...
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predict2*10000);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ============= Part 4: Visualizing J(theta_0, theta_1) =============
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fprintf('Visualizing J(theta_0, theta_1) ...\n')
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% Grid over which we will calculate J
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theta0_vals = linspace(-10, 10, 100);
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theta1_vals = linspace(-1, 4, 100);
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% initialize J_vals to a matrix of 0's
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J_vals = zeros(length(theta0_vals), length(theta1_vals));
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% Fill out J_vals
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for i = 1:length(theta0_vals)
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for j = 1:length(theta1_vals)
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t = [theta0_vals(i); theta1_vals(j)];
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J_vals(i,j) = computeCost(X, y, t);
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end
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end
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% Because of the way meshgrids work in the surf command, we need to
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% transpose J_vals before calling surf, or else the axes will be flipped
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J_vals = J_vals';
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% Surface plot
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figure;
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surf(theta0_vals, theta1_vals, J_vals)
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xlabel('\theta_0'); ylabel('\theta_1');
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% Contour plot
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figure;
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% Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100
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contour(theta0_vals, theta1_vals, J_vals, logspace(-2, 3, 20))
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xlabel('\theta_0'); ylabel('\theta_1');
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hold on;
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plot(theta(1), theta(2), 'rx', 'MarkerSize', 10, 'LineWidth', 2);
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167
ds/25-1/r/6/mlclass-ex1/ex1_multi.m
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ds/25-1/r/6/mlclass-ex1/ex1_multi.m
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%% Machine Learning Online Class
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% Exercise 1: Linear regression with multiple variables
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%
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% Instructions
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% ------------
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%
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% This file contains code that helps you get started on the
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% linear regression exercise.
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%
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% You will need to complete the following functions in this
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% exericse:
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%
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% warmUpExercise.m
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% plotData.m
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% gradientDescent.m
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% computeCost.m
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% gradientDescentMulti.m
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% computeCostMulti.m
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% featureNormalize.m
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% normalEqn.m
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%
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% For this part of the exercise, you will need to change some
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% parts of the code below for various experiments (e.g., changing
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% learning rates).
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%
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%% Initialization
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%% ================ Part 1: Feature Normalization ================
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%% Clear and Close Figures
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clear all; close all; clc
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fprintf('Loading data ...\n');
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%% Load Data
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data = load('ex1data2.txt');
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X = data(:, 1:2);
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y = data(:, 3);
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m = length(y);
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% Print out some data points
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fprintf('First 10 examples from the dataset: \n');
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fprintf(' x = [%.0f %.0f], y = %.0f \n', [X(1:10,:) y(1:10,:)]');
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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% Scale features and set them to zero mean
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fprintf('Normalizing Features ...\n');
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[X mu sigma] = featureNormalize(X);
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fprintf('mu\n');
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fprintf(' %f \n', mu);
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fprintf('sigma\n');
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fprintf(' %f \n', sigma);
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% Add intercept term to X
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X = [ones(m, 1) X];
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fprintf('Normalized X\n');
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fprintf(' [%f, %f, %f] \n', X(1:10,:)');
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%% ================ Part 2: Gradient Descent ================
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% ====================== YOUR CODE HERE ======================
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% Instructions: We have provided you with the following starter
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% code that runs gradient descent with a particular
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% learning rate (alpha).
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%
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% Your task is to first make sure that your functions -
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% computeCost and gradientDescent already work with
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% this starter code and support multiple variables.
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%
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% After that, try running gradient descent with
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% different values of alpha and see which one gives
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% you the best result.
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%
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% Finally, you should complete the code at the end
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% to predict the price of a 1650 sq-ft, 3 br house.
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%
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% Hint: By using the 'hold on' command, you can plot multiple
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% graphs on the same figure.
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%
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% Hint: At prediction, make sure you do the same feature normalization.
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%
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fprintf('Running gradient descent ...\n');
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% Choose some alpha value
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alpha = 0.01;
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num_iters = 50;
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% Init Theta and Run Gradient Descent
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theta = zeros(3, 1);
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[theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters);
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% Plot the convergence graph
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figure;
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plot(1:numel(J_history), J_history, '-b', 'LineWidth', 2);
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xlabel('Number of iterations');
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ylabel('Cost J');
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% Display gradient descent's result
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fprintf('Theta computed from gradient descent: \n');
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fprintf(' %f \n', theta);
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fprintf('\n');
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% Estimate the price of a 1650 sq-ft, 3 br house
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% ====================== YOUR CODE HERE ======================
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% Recall that the first column of X is all-ones. Thus, it does
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% not need to be normalized.
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normed_row = ([1650, 3] - mu) ./ sigma;
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xrow = [1, normed_row];
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price = xrow * theta;
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% ============================================================
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fprintf(['Predicted price of a 1650 sq-ft, 3 br house ' ...
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'(using gradient descent):\n $%f\n'], price);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ================ Part 3: Normal Equations ================
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fprintf('Solving with normal equations...\n');
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% ====================== YOUR CODE HERE ======================
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% Instructions: The following code computes the closed form
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% solution for linear regression using the normal
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% equations. You should complete the code in
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% normalEqn.m
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%
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% After doing so, you should complete this code
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% to predict the price of a 1650 sq-ft, 3 br house.
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|
%
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%% Load Data
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data = csvread('ex1data2.txt');
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X = data(:, 1:2);
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y = data(:, 3);
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m = length(y);
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% Add intercept term to X
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X = [ones(m, 1) X];
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% Calculate the parameters from the normal equation
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theta = normalEqn(X, y);
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% Display normal equation's result
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fprintf('Theta computed from the normal equations: \n');
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fprintf(' %f \n', theta);
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fprintf('\n');
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% Estimate the price of a 1650 sq-ft, 3 br house
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% ====================== YOUR CODE HERE ======================
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|
price = [1, 1650, 3] * theta; % You should change this
|
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|
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|
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% ============================================================
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fprintf(['Predicted price of a 1650 sq-ft, 3 br house ' ...
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|
'(using normal equations):\n $%f\n'], price);
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97
ds/25-1/r/6/mlclass-ex1/ex1data1.txt
Normal file
97
ds/25-1/r/6/mlclass-ex1/ex1data1.txt
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6.1101,17.592
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|
5.5277,9.1302
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|
8.5186,13.662
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|
7.0032,11.854
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||||||
|
5.8598,6.8233
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||||||
|
8.3829,11.886
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|
7.4764,4.3483
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|
8.5781,12
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|
6.4862,6.5987
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|
5.0546,3.8166
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||||||
|
5.7107,3.2522
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||||||
|
14.164,15.505
|
||||||
|
5.734,3.1551
|
||||||
|
8.4084,7.2258
|
||||||
|
5.6407,0.71618
|
||||||
|
5.3794,3.5129
|
||||||
|
6.3654,5.3048
|
||||||
|
5.1301,0.56077
|
||||||
|
6.4296,3.6518
|
||||||
|
7.0708,5.3893
|
||||||
|
6.1891,3.1386
|
||||||
|
20.27,21.767
|
||||||
|
5.4901,4.263
|
||||||
|
6.3261,5.1875
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||||||
|
5.5649,3.0825
|
||||||
|
18.945,22.638
|
||||||
|
12.828,13.501
|
||||||
|
10.957,7.0467
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||||||
|
13.176,14.692
|
||||||
|
22.203,24.147
|
||||||
|
5.2524,-1.22
|
||||||
|
6.5894,5.9966
|
||||||
|
9.2482,12.134
|
||||||
|
5.8918,1.8495
|
||||||
|
8.2111,6.5426
|
||||||
|
7.9334,4.5623
|
||||||
|
8.0959,4.1164
|
||||||
|
5.6063,3.3928
|
||||||
|
12.836,10.117
|
||||||
|
6.3534,5.4974
|
||||||
|
5.4069,0.55657
|
||||||
|
6.8825,3.9115
|
||||||
|
11.708,5.3854
|
||||||
|
5.7737,2.4406
|
||||||
|
7.8247,6.7318
|
||||||
|
7.0931,1.0463
|
||||||
|
5.0702,5.1337
|
||||||
|
5.8014,1.844
|
||||||
|
11.7,8.0043
|
||||||
|
5.5416,1.0179
|
||||||
|
7.5402,6.7504
|
||||||
|
5.3077,1.8396
|
||||||
|
7.4239,4.2885
|
||||||
|
7.6031,4.9981
|
||||||
|
6.3328,1.4233
|
||||||
|
6.3589,-1.4211
|
||||||
|
6.2742,2.4756
|
||||||
|
5.6397,4.6042
|
||||||
|
9.3102,3.9624
|
||||||
|
9.4536,5.4141
|
||||||
|
8.8254,5.1694
|
||||||
|
5.1793,-0.74279
|
||||||
|
21.279,17.929
|
||||||
|
14.908,12.054
|
||||||
|
18.959,17.054
|
||||||
|
7.2182,4.8852
|
||||||
|
8.2951,5.7442
|
||||||
|
10.236,7.7754
|
||||||
|
5.4994,1.0173
|
||||||
|
20.341,20.992
|
||||||
|
10.136,6.6799
|
||||||
|
7.3345,4.0259
|
||||||
|
6.0062,1.2784
|
||||||
|
7.2259,3.3411
|
||||||
|
5.0269,-2.6807
|
||||||
|
6.5479,0.29678
|
||||||
|
7.5386,3.8845
|
||||||
|
5.0365,5.7014
|
||||||
|
10.274,6.7526
|
||||||
|
5.1077,2.0576
|
||||||
|
5.7292,0.47953
|
||||||
|
5.1884,0.20421
|
||||||
|
6.3557,0.67861
|
||||||
|
9.7687,7.5435
|
||||||
|
6.5159,5.3436
|
||||||
|
8.5172,4.2415
|
||||||
|
9.1802,6.7981
|
||||||
|
6.002,0.92695
|
||||||
|
5.5204,0.152
|
||||||
|
5.0594,2.8214
|
||||||
|
5.7077,1.8451
|
||||||
|
7.6366,4.2959
|
||||||
|
5.8707,7.2029
|
||||||
|
5.3054,1.9869
|
||||||
|
8.2934,0.14454
|
||||||
|
13.394,9.0551
|
||||||
|
5.4369,0.61705
|
||||||
47
ds/25-1/r/6/mlclass-ex1/ex1data2.txt
Normal file
47
ds/25-1/r/6/mlclass-ex1/ex1data2.txt
Normal file
@ -0,0 +1,47 @@
|
|||||||
|
2104,3,399900
|
||||||
|
1600,3,329900
|
||||||
|
2400,3,369000
|
||||||
|
1416,2,232000
|
||||||
|
3000,4,539900
|
||||||
|
1985,4,299900
|
||||||
|
1534,3,314900
|
||||||
|
1427,3,198999
|
||||||
|
1380,3,212000
|
||||||
|
1494,3,242500
|
||||||
|
1940,4,239999
|
||||||
|
2000,3,347000
|
||||||
|
1890,3,329999
|
||||||
|
4478,5,699900
|
||||||
|
1268,3,259900
|
||||||
|
2300,4,449900
|
||||||
|
1320,2,299900
|
||||||
|
1236,3,199900
|
||||||
|
2609,4,499998
|
||||||
|
3031,4,599000
|
||||||
|
1767,3,252900
|
||||||
|
1888,2,255000
|
||||||
|
1604,3,242900
|
||||||
|
1962,4,259900
|
||||||
|
3890,3,573900
|
||||||
|
1100,3,249900
|
||||||
|
1458,3,464500
|
||||||
|
2526,3,469000
|
||||||
|
2200,3,475000
|
||||||
|
2637,3,299900
|
||||||
|
1839,2,349900
|
||||||
|
1000,1,169900
|
||||||
|
2040,4,314900
|
||||||
|
3137,3,579900
|
||||||
|
1811,4,285900
|
||||||
|
1437,3,249900
|
||||||
|
1239,3,229900
|
||||||
|
2132,4,345000
|
||||||
|
4215,4,549000
|
||||||
|
2162,4,287000
|
||||||
|
1664,2,368500
|
||||||
|
2238,3,329900
|
||||||
|
2567,4,314000
|
||||||
|
1200,3,299000
|
||||||
|
852,2,179900
|
||||||
|
1852,4,299900
|
||||||
|
1203,3,239500
|
||||||
35
ds/25-1/r/6/mlclass-ex1/featureNormalize.m
Normal file
35
ds/25-1/r/6/mlclass-ex1/featureNormalize.m
Normal file
@ -0,0 +1,35 @@
|
|||||||
|
function [X_norm, mu, sigma] = featureNormalize(X)
|
||||||
|
%FEATURENORMALIZE Normalizes the features in X
|
||||||
|
% FEATURENORMALIZE(X) returns a normalized version of X where
|
||||||
|
% the mean value of each feature is 0 and the standard deviation
|
||||||
|
% is 1. This is often a good preprocessing step to do when
|
||||||
|
% working with learning algorithms.
|
||||||
|
|
||||||
|
% You need to set these values correctly
|
||||||
|
X_norm = X;
|
||||||
|
mu = zeros(1, size(X, 2));
|
||||||
|
sigma = zeros(1, size(X, 2));
|
||||||
|
|
||||||
|
% ====================== YOUR CODE HERE ======================
|
||||||
|
% Instructions: First, for each feature dimension, compute the mean
|
||||||
|
% of the feature and subtract it from the dataset,
|
||||||
|
% storing the mean value in mu. Next, compute the
|
||||||
|
% standard deviation of each feature and divide
|
||||||
|
% each feature by it's standard deviation, storing
|
||||||
|
% the standard deviation in sigma.
|
||||||
|
%
|
||||||
|
% Note that X is a matrix where each column is a
|
||||||
|
% feature and each row is an example. You need
|
||||||
|
% to perform the normalization separately for
|
||||||
|
% each feature.
|
||||||
|
%
|
||||||
|
% Hint: You might find the 'mean' and 'std' functions useful.
|
||||||
|
%
|
||||||
|
|
||||||
|
mu = mean(X);
|
||||||
|
sigma = std(X);
|
||||||
|
X_norm = (X - mu) ./ sigma;
|
||||||
|
|
||||||
|
% ============================================================
|
||||||
|
|
||||||
|
end
|
||||||
34
ds/25-1/r/6/mlclass-ex1/gradientDescent.m
Normal file
34
ds/25-1/r/6/mlclass-ex1/gradientDescent.m
Normal file
@ -0,0 +1,34 @@
|
|||||||
|
function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
|
||||||
|
%GRADIENTDESCENT Performs gradient descent to learn theta
|
||||||
|
% theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by
|
||||||
|
% taking num_iters gradient steps with learning rate alpha
|
||||||
|
|
||||||
|
% Initialize some useful values
|
||||||
|
m = length(y); % number of training examples
|
||||||
|
J_history = zeros(num_iters, 1);
|
||||||
|
|
||||||
|
for iter = 1:num_iters
|
||||||
|
|
||||||
|
% ====================== YOUR CODE HERE ======================
|
||||||
|
% Instructions: Perform a single gradient step on the parameter vector
|
||||||
|
% theta.
|
||||||
|
%
|
||||||
|
% Hint: While debugging, it can be useful to print out the values
|
||||||
|
% of the cost function (computeCost) and gradient here.
|
||||||
|
%
|
||||||
|
% X: (m, 2)
|
||||||
|
% y: (m, 1)
|
||||||
|
% theta: (2, 1)
|
||||||
|
|
||||||
|
h = X * theta;
|
||||||
|
base = ((h - y)' * X)';
|
||||||
|
theta -= alpha / m * base;
|
||||||
|
|
||||||
|
% ============================================================
|
||||||
|
|
||||||
|
% Save the cost J in every iteration
|
||||||
|
J_history(iter) = computeCost(X, y, theta);
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
end
|
||||||
31
ds/25-1/r/6/mlclass-ex1/gradientDescentMulti.m
Normal file
31
ds/25-1/r/6/mlclass-ex1/gradientDescentMulti.m
Normal file
@ -0,0 +1,31 @@
|
|||||||
|
function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
|
||||||
|
%GRADIENTDESCENTMULTI Performs gradient descent to learn theta
|
||||||
|
% theta = GRADIENTDESCENTMULTI(x, y, theta, alpha, num_iters) updates theta by
|
||||||
|
% taking num_iters gradient steps with learning rate alpha
|
||||||
|
|
||||||
|
% Initialize some useful values
|
||||||
|
m = length(y); % number of training examples
|
||||||
|
J_history = zeros(num_iters, 1);
|
||||||
|
|
||||||
|
for iter = 1:num_iters
|
||||||
|
|
||||||
|
% ====================== YOUR CODE HERE ======================
|
||||||
|
% Instructions: Perform a single gradient step on the parameter vector
|
||||||
|
% theta.
|
||||||
|
%
|
||||||
|
% Hint: While debugging, it can be useful to print out the values
|
||||||
|
% of the cost function (computeCostMulti) and gradient here.
|
||||||
|
%
|
||||||
|
|
||||||
|
h = X * theta;
|
||||||
|
base = ((h - y)' * X)';
|
||||||
|
theta -= alpha / m * base;
|
||||||
|
|
||||||
|
% ============================================================
|
||||||
|
|
||||||
|
% Save the cost J in every iteration
|
||||||
|
J_history(iter) = computeCostMulti(X, y, theta);
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
end
|
||||||
23
ds/25-1/r/6/mlclass-ex1/normalEqn.m
Normal file
23
ds/25-1/r/6/mlclass-ex1/normalEqn.m
Normal file
@ -0,0 +1,23 @@
|
|||||||
|
function [theta] = normalEqn(X, y)
|
||||||
|
%NORMALEQN Computes the closed-form solution to linear regression
|
||||||
|
% NORMALEQN(X,y) computes the closed-form solution to linear
|
||||||
|
% regression using the normal equations.
|
||||||
|
|
||||||
|
theta = zeros(size(X, 2), 1);
|
||||||
|
|
||||||
|
% ====================== YOUR CODE HERE ======================
|
||||||
|
% Instructions: Complete the code to compute the closed form solution
|
||||||
|
% to linear regression and put the result in theta.
|
||||||
|
%
|
||||||
|
|
||||||
|
% ---------------------- Sample Solution ----------------------
|
||||||
|
|
||||||
|
theta = inv(X' * X) * X' * y
|
||||||
|
|
||||||
|
|
||||||
|
% -------------------------------------------------------------
|
||||||
|
|
||||||
|
|
||||||
|
% ============================================================
|
||||||
|
|
||||||
|
end
|
||||||
25
ds/25-1/r/6/mlclass-ex1/plotData.m
Normal file
25
ds/25-1/r/6/mlclass-ex1/plotData.m
Normal file
@ -0,0 +1,25 @@
|
|||||||
|
function plotData(x, y)
|
||||||
|
%PLOTDATA Plots the data points x and y into a new figure
|
||||||
|
% PLOTDATA(x,y) plots the data points and gives the figure axes labels of
|
||||||
|
% population and profit.
|
||||||
|
|
||||||
|
% ====================== YOUR CODE HERE ======================
|
||||||
|
% Instructions: Plot the training data into a figure using the
|
||||||
|
% "figure" and "plot" commands. Set the axes labels using
|
||||||
|
% the "xlabel" and "ylabel" commands. Assume the
|
||||||
|
% population and revenue data have been passed in
|
||||||
|
% as the x and y arguments of this function.
|
||||||
|
%
|
||||||
|
% Hint: You can use the 'rx' option with plot to have the markers
|
||||||
|
% appear as red crosses. Furthermore, you can make the
|
||||||
|
% markers larger by using plot(..., 'rx', 'MarkerSize', 10);
|
||||||
|
|
||||||
|
figure; % open a new figure window
|
||||||
|
|
||||||
|
plot(x, y, 'rx', 'markersize', 10)
|
||||||
|
ylabel('profit in 10000')
|
||||||
|
xlabel('population in 10000')
|
||||||
|
|
||||||
|
% ============================================================
|
||||||
|
|
||||||
|
end
|
||||||
336
ds/25-1/r/6/mlclass-ex1/submit.m
Normal file
336
ds/25-1/r/6/mlclass-ex1/submit.m
Normal file
@ -0,0 +1,336 @@
|
|||||||
|
function submit(part)
|
||||||
|
%SUBMIT Submit your code and output to the ml-class servers
|
||||||
|
% SUBMIT() will connect to the ml-class server and submit your solution
|
||||||
|
|
||||||
|
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
|
||||||
|
homework_id());
|
||||||
|
if ~exist('part', 'var') || isempty(part)
|
||||||
|
partId = promptPart();
|
||||||
|
end
|
||||||
|
|
||||||
|
% Check valid partId
|
||||||
|
partNames = validParts();
|
||||||
|
if ~isValidPartId(partId)
|
||||||
|
fprintf('!! Invalid homework part selected.\n');
|
||||||
|
fprintf('!! Expected an integer from 1 to %d.\n', numel(partNames) + 1);
|
||||||
|
fprintf('!! Submission Cancelled\n');
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
[login password] = loginPrompt();
|
||||||
|
if isempty(login)
|
||||||
|
fprintf('!! Submission Cancelled\n');
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
fprintf('\n== Connecting to ml-class ... ');
|
||||||
|
if exist('OCTAVE_VERSION')
|
||||||
|
fflush(stdout);
|
||||||
|
end
|
||||||
|
|
||||||
|
% Setup submit list
|
||||||
|
if partId == numel(partNames) + 1
|
||||||
|
submitParts = 1:numel(partNames);
|
||||||
|
else
|
||||||
|
submitParts = [partId];
|
||||||
|
end
|
||||||
|
|
||||||
|
for s = 1:numel(submitParts)
|
||||||
|
% Submit this part
|
||||||
|
partId = submitParts(s);
|
||||||
|
|
||||||
|
% Get Challenge
|
||||||
|
[login, ch, signature] = getChallenge(login);
|
||||||
|
if isempty(login) || isempty(ch) || isempty(signature)
|
||||||
|
% Some error occured, error string in first return element.
|
||||||
|
fprintf('\n!! Error: %s\n\n', login);
|
||||||
|
return
|
||||||
|
end
|
||||||
|
|
||||||
|
% Attempt Submission with Challenge
|
||||||
|
ch_resp = challengeResponse(login, password, ch);
|
||||||
|
[result, str] = submitSolution(login, ch_resp, partId, output(partId), ...
|
||||||
|
source(partId), signature);
|
||||||
|
|
||||||
|
fprintf('\n== [ml-class] Submitted Homework %s - Part %d - %s\n', ...
|
||||||
|
homework_id(), partId, partNames{partId});
|
||||||
|
fprintf('== %s\n', strtrim(str));
|
||||||
|
if exist('OCTAVE_VERSION')
|
||||||
|
fflush(stdout);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
% ================== CONFIGURABLES FOR EACH HOMEWORK ==================
|
||||||
|
|
||||||
|
function id = homework_id()
|
||||||
|
id = '1';
|
||||||
|
end
|
||||||
|
|
||||||
|
function [partNames] = validParts()
|
||||||
|
partNames = { 'Warm up exercise ', ...
|
||||||
|
'Computing Cost (for one variable)', ...
|
||||||
|
'Gradient Descent (for one variable)', ...
|
||||||
|
'Feature Normalization', ...
|
||||||
|
'Computing Cost (for multiple variables)', ...
|
||||||
|
'Gradient Descent (for multiple variables)', ...
|
||||||
|
'Normal Equations'};
|
||||||
|
end
|
||||||
|
|
||||||
|
function srcs = sources()
|
||||||
|
% Separated by part
|
||||||
|
srcs = { { 'warmUpExercise.m' }, ...
|
||||||
|
{ 'computeCost.m' }, ...
|
||||||
|
{ 'gradientDescent.m' }, ...
|
||||||
|
{ 'featureNormalize.m' }, ...
|
||||||
|
{ 'computeCostMulti.m' }, ...
|
||||||
|
{ 'gradientDescentMulti.m' }, ...
|
||||||
|
{ 'normalEqn.m' }, ...
|
||||||
|
};
|
||||||
|
end
|
||||||
|
|
||||||
|
function out = output(partId)
|
||||||
|
% Random Test Cases
|
||||||
|
X1 = [ones(20,1) (exp(1) + exp(2) * (0.1:0.1:2))'];
|
||||||
|
Y1 = X1(:,2) + sin(X1(:,1)) + cos(X1(:,2));
|
||||||
|
X2 = [X1 X1(:,2).^0.5 X1(:,2).^0.25];
|
||||||
|
Y2 = Y1.^0.5 + Y1;
|
||||||
|
if partId == 1
|
||||||
|
out = sprintf('%0.5f ', warmUpExercise());
|
||||||
|
elseif partId == 2
|
||||||
|
out = sprintf('%0.5f ', computeCost(X1, Y1, [0.5 -0.5]'));
|
||||||
|
elseif partId == 3
|
||||||
|
out = sprintf('%0.5f ', gradientDescent(X1, Y1, [0.5 -0.5]', 0.01, 10));
|
||||||
|
elseif partId == 4
|
||||||
|
out = sprintf('%0.5f ', featureNormalize(X2(:,2:4)));
|
||||||
|
elseif partId == 5
|
||||||
|
out = sprintf('%0.5f ', computeCostMulti(X2, Y2, [0.1 0.2 0.3 0.4]'));
|
||||||
|
elseif partId == 6
|
||||||
|
out = sprintf('%0.5f ', gradientDescentMulti(X2, Y2, [-0.1 -0.2 -0.3 -0.4]', 0.01, 10));
|
||||||
|
elseif partId == 7
|
||||||
|
out = sprintf('%0.5f ', normalEqn(X2, Y2));
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function url = challenge_url()
|
||||||
|
url = 'http://www.ml-class.org/course/homework/challenge';
|
||||||
|
end
|
||||||
|
|
||||||
|
function url = submit_url()
|
||||||
|
url = 'http://www.ml-class.org/course/homework/submit';
|
||||||
|
end
|
||||||
|
|
||||||
|
% ========================= CHALLENGE HELPERS =========================
|
||||||
|
|
||||||
|
function src = source(partId)
|
||||||
|
src = '';
|
||||||
|
src_files = sources();
|
||||||
|
if partId <= numel(src_files)
|
||||||
|
flist = src_files{partId};
|
||||||
|
for i = 1:numel(flist)
|
||||||
|
fid = fopen(flist{i});
|
||||||
|
while ~feof(fid)
|
||||||
|
line = fgets(fid);
|
||||||
|
src = [src line];
|
||||||
|
end
|
||||||
|
src = [src '||||||||'];
|
||||||
|
end
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function ret = isValidPartId(partId)
|
||||||
|
partNames = validParts();
|
||||||
|
ret = (~isempty(partId)) && (partId >= 1) && (partId <= numel(partNames) + 1);
|
||||||
|
end
|
||||||
|
|
||||||
|
function partId = promptPart()
|
||||||
|
fprintf('== Select which part(s) to submit:\n', ...
|
||||||
|
homework_id());
|
||||||
|
partNames = validParts();
|
||||||
|
srcFiles = sources();
|
||||||
|
for i = 1:numel(partNames)
|
||||||
|
fprintf('== %d) %s [', i, partNames{i});
|
||||||
|
fprintf(' %s ', srcFiles{i}{:});
|
||||||
|
fprintf(']\n');
|
||||||
|
end
|
||||||
|
fprintf('== %d) All of the above \n==\nEnter your choice [1-%d]: ', ...
|
||||||
|
numel(partNames) + 1, numel(partNames) + 1);
|
||||||
|
selPart = input('', 's');
|
||||||
|
partId = str2num(selPart);
|
||||||
|
if ~isValidPartId(partId)
|
||||||
|
partId = -1;
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function [email,ch,signature] = getChallenge(email)
|
||||||
|
str = urlread(challenge_url(), 'post', {'email_address', email});
|
||||||
|
|
||||||
|
str = strtrim(str);
|
||||||
|
[email, str] = strtok (str, '|');
|
||||||
|
[ch, str] = strtok (str, '|');
|
||||||
|
[signature, str] = strtok (str, '|');
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
function [result, str] = submitSolution(email, ch_resp, part, output, ...
|
||||||
|
source, signature)
|
||||||
|
|
||||||
|
params = {'homework', homework_id(), ...
|
||||||
|
'part', num2str(part), ...
|
||||||
|
'email', email, ...
|
||||||
|
'output', output, ...
|
||||||
|
'source', source, ...
|
||||||
|
'challenge_response', ch_resp, ...
|
||||||
|
'signature', signature};
|
||||||
|
|
||||||
|
str = urlread(submit_url(), 'post', params);
|
||||||
|
|
||||||
|
% Parse str to read for success / failure
|
||||||
|
result = 0;
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
% =========================== LOGIN HELPERS ===========================
|
||||||
|
|
||||||
|
function [login password] = loginPrompt()
|
||||||
|
% Prompt for password
|
||||||
|
[login password] = basicPrompt();
|
||||||
|
|
||||||
|
if isempty(login) || isempty(password)
|
||||||
|
login = []; password = [];
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
function [login password] = basicPrompt()
|
||||||
|
login = input('Login (Email address): ', 's');
|
||||||
|
password = input('Password: ', 's');
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
function [str] = challengeResponse(email, passwd, challenge)
|
||||||
|
salt = ')~/|]QMB3[!W`?OVt7qC"@+}';
|
||||||
|
str = sha1([challenge sha1([salt email passwd])]);
|
||||||
|
sel = randperm(numel(str));
|
||||||
|
sel = sort(sel(1:16));
|
||||||
|
str = str(sel);
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
% =============================== SHA-1 ================================
|
||||||
|
|
||||||
|
function hash = sha1(str)
|
||||||
|
|
||||||
|
% Initialize variables
|
||||||
|
h0 = uint32(1732584193);
|
||||||
|
h1 = uint32(4023233417);
|
||||||
|
h2 = uint32(2562383102);
|
||||||
|
h3 = uint32(271733878);
|
||||||
|
h4 = uint32(3285377520);
|
||||||
|
|
||||||
|
% Convert to word array
|
||||||
|
strlen = numel(str);
|
||||||
|
|
||||||
|
% Break string into chars and append the bit 1 to the message
|
||||||
|
mC = [double(str) 128];
|
||||||
|
mC = [mC zeros(1, 4-mod(numel(mC), 4), 'uint8')];
|
||||||
|
|
||||||
|
numB = strlen * 8;
|
||||||
|
if exist('idivide')
|
||||||
|
numC = idivide(uint32(numB + 65), 512, 'ceil');
|
||||||
|
else
|
||||||
|
numC = ceil(double(numB + 65)/512);
|
||||||
|
end
|
||||||
|
numW = numC * 16;
|
||||||
|
mW = zeros(numW, 1, 'uint32');
|
||||||
|
|
||||||
|
idx = 1;
|
||||||
|
for i = 1:4:strlen + 1
|
||||||
|
mW(idx) = bitor(bitor(bitor( ...
|
||||||
|
bitshift(uint32(mC(i)), 24), ...
|
||||||
|
bitshift(uint32(mC(i+1)), 16)), ...
|
||||||
|
bitshift(uint32(mC(i+2)), 8)), ...
|
||||||
|
uint32(mC(i+3)));
|
||||||
|
idx = idx + 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
% Append length of message
|
||||||
|
mW(numW - 1) = uint32(bitshift(uint64(numB), -32));
|
||||||
|
mW(numW) = uint32(bitshift(bitshift(uint64(numB), 32), -32));
|
||||||
|
|
||||||
|
% Process the message in successive 512-bit chs
|
||||||
|
for cId = 1 : double(numC)
|
||||||
|
cSt = (cId - 1) * 16 + 1;
|
||||||
|
cEnd = cId * 16;
|
||||||
|
ch = mW(cSt : cEnd);
|
||||||
|
|
||||||
|
% Extend the sixteen 32-bit words into eighty 32-bit words
|
||||||
|
for j = 17 : 80
|
||||||
|
ch(j) = ch(j - 3);
|
||||||
|
ch(j) = bitxor(ch(j), ch(j - 8));
|
||||||
|
ch(j) = bitxor(ch(j), ch(j - 14));
|
||||||
|
ch(j) = bitxor(ch(j), ch(j - 16));
|
||||||
|
ch(j) = bitrotate(ch(j), 1);
|
||||||
|
end
|
||||||
|
|
||||||
|
% Initialize hash value for this ch
|
||||||
|
a = h0;
|
||||||
|
b = h1;
|
||||||
|
c = h2;
|
||||||
|
d = h3;
|
||||||
|
e = h4;
|
||||||
|
|
||||||
|
% Main loop
|
||||||
|
for i = 1 : 80
|
||||||
|
if(i >= 1 && i <= 20)
|
||||||
|
f = bitor(bitand(b, c), bitand(bitcmp(b), d));
|
||||||
|
k = uint32(1518500249);
|
||||||
|
elseif(i >= 21 && i <= 40)
|
||||||
|
f = bitxor(bitxor(b, c), d);
|
||||||
|
k = uint32(1859775393);
|
||||||
|
elseif(i >= 41 && i <= 60)
|
||||||
|
f = bitor(bitor(bitand(b, c), bitand(b, d)), bitand(c, d));
|
||||||
|
k = uint32(2400959708);
|
||||||
|
elseif(i >= 61 && i <= 80)
|
||||||
|
f = bitxor(bitxor(b, c), d);
|
||||||
|
k = uint32(3395469782);
|
||||||
|
end
|
||||||
|
|
||||||
|
t = bitrotate(a, 5);
|
||||||
|
t = bitadd(t, f);
|
||||||
|
t = bitadd(t, e);
|
||||||
|
t = bitadd(t, k);
|
||||||
|
t = bitadd(t, ch(i));
|
||||||
|
e = d;
|
||||||
|
d = c;
|
||||||
|
c = bitrotate(b, 30);
|
||||||
|
b = a;
|
||||||
|
a = t;
|
||||||
|
|
||||||
|
end
|
||||||
|
h0 = bitadd(h0, a);
|
||||||
|
h1 = bitadd(h1, b);
|
||||||
|
h2 = bitadd(h2, c);
|
||||||
|
h3 = bitadd(h3, d);
|
||||||
|
h4 = bitadd(h4, e);
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
hash = reshape(dec2hex(double([h0 h1 h2 h3 h4]), 8)', [1 40]);
|
||||||
|
|
||||||
|
hash = lower(hash);
|
||||||
|
|
||||||
|
end
|
||||||
|
|
||||||
|
function ret = bitadd(iA, iB)
|
||||||
|
ret = double(iA) + double(iB);
|
||||||
|
ret = bitset(ret, 33, 0);
|
||||||
|
ret = uint32(ret);
|
||||||
|
end
|
||||||
|
|
||||||
|
function ret = bitrotate(iA, places)
|
||||||
|
t = bitshift(iA, places - 32);
|
||||||
|
ret = bitshift(iA, places);
|
||||||
|
ret = bitor(ret, t);
|
||||||
|
end
|
||||||
15
ds/25-1/r/6/mlclass-ex1/warmUpExercise.m
Normal file
15
ds/25-1/r/6/mlclass-ex1/warmUpExercise.m
Normal file
@ -0,0 +1,15 @@
|
|||||||
|
function A = warmUpExercise()
|
||||||
|
|
||||||
|
% Instructions: Return the 5x5 identity matrix
|
||||||
|
% In octave, we return values by defining which variables
|
||||||
|
% represent the return values (at the top of the file)
|
||||||
|
% and then set them accordingly.
|
||||||
|
A = zeros(5, 5);
|
||||||
|
for i=1:5
|
||||||
|
A(i, i) = 1;
|
||||||
|
end
|
||||||
|
|
||||||
|
% ===========================================
|
||||||
|
|
||||||
|
|
||||||
|
end
|
||||||
Reference in New Issue
Block a user