吳恩達 機器學習 ex1
阿新 • • 發佈:2018-11-19
clear ; close all; clc %初始化 fprintf('Running warmUpExercise ... \n'); fprintf('5x5 Identity Matrix: \n'); warmUpExercise() fprintf('Program paused. Press enter to continue.\n'); pause; fprintf('Plotting Data ...\n') data = load('ex1data1.txt'); %讀取離散data x = data(:, 1); y = data(:, 2); m = length(y); %訓練number plot(x, y,'rx','MarkerSize',10); %plot yhe data ylabel('Profit in $10,000s'); %set the y-axis label xlabel('Population of City in 10,000s'); %set the x-axis labe fprintf('Program paused. Press enter to continue.\n'); pause;
plot 用法
plot - 二維線圖
此 MATLAB 函式 建立 Y 中資料對 X 中對應值的二維線圖。 如果 X 和 Y 都是向量,則它們的長度必須相同。plot 函式繪製 Y 對 X 的圖。
如果 X 和 Y 均為矩陣,則它們的大小必須相同。plot 函式繪製 Y 的列對 X 的列的圖。 如果 X 或 Y中的一個是向量而另一個是矩陣,則矩陣的各維中必須有一維與向量的長度相等。如果矩陣的行數等於向量長度,則 plot
函式繪製矩陣中的每一列對向量的圖。如果矩陣的列數等於向量長度,則該函式繪製矩陣中的每一行對向量的圖。如果矩陣為方陣,則該函式繪製每一列對向量的圖。 如果 X 或Y 之一為標量,而另一個為標量或向量,則 plot 函式會繪製離散點。但是,要檢視這些點,您必須指定標記符號,例如 plot(X,Y,'o')。
plot(X,Y)
plot(X,Y,LineSpec)
plot(X1,Y1,...,Xn,Yn)
plot(X1,Y1,LineSpec1,...,Xn,Yn,LineSpecn)
plot(Y)
plot(Y,LineSpec)
plot(___,Name,Value)
plot(ax,___)
h = plot(___)
另請參閱 gca, hold, legend, loglog, plot3, title, xlabel, xlim, ylabel, ylim,
yyaxis, Line 屬性
x = [ones(m, 1), data(:,1)]; % Add a column of ones to x (m個1)
theta = zeros(2, 1); % initialize fitting parameters(初始化擬合引數)
% Some gradient descent settings
iterations = 1500; %迭代次數
alpha = 0.01; %學習率
fprintf('\nTesting the cost function ...\n')
% compute and display initial cost
J = computeCost(x, y, theta); %代價函式
fprintf('With theta = [0 ; 0]\nCost computed = %f\n', J);
fprintf('Expected cost value (approx) 32.07\n');
% further testing of the cost function(進一步測試)
J = computeCost(x, y, [-1 ; 2]);
fprintf('\nWith theta = [-1 ; 2]\nCost computed = %f\n', J);
fprintf('Expected cost value (approx) 54.24\n');
fprintf('Program paused. Press enter to continue.\n');
pause;
fprintf('\nRunning Gradient Descent ...\n')
% run gradient descent(梯度下降法)
theta = gradientDescent(x, y, theta, alpha, iterations);
% print theta to screen
fprintf('Theta found by gradient descent:\n');
fprintf('%f\n', theta);
fprintf('Expected theta values (approx)\n');
fprintf(' -3.6303\n 1.1664\n\n');
% Plot the linear fit
hold on; % keep previous plot visible(保持現有的影象)
plot(x(:,2), x*theta, '-') % x軸=x矩陣的第二列,y軸=x*theta
legend('Training data', 'Linear regression')
hold off % don't overlay any more plots on this figure
% Predict values for population sizes of 35,000 and 70,000
predict1 = [1, 3.5] *theta;
fprintf('For population = 35,000, we predict a profit of %f\n',...
predict1*10000);
predict2 = [1, 7] * theta;
fprintf('For population = 70,000, we predict a profit of %f\n',...
predict2*10000);
fprintf('Program paused. Press enter to continue.\n');
pause;
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
% Initialize some useful values
m = length(y); % number of training examples
% You need to return the following variables correctly
J = 0;
% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta
% You should set J to the cost.
h = x*theta - y;
J = 1/(2*m) * sum(h.^2);
% =========================================================================
end
Gradient descent
function [theta, J_history] = gradientDescent(x, y, theta, alpha, num_iters)
%GRADIENTDESCENT Performs gradient descent to learn theta
% theta = GRADIENTDESCENT(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.
%
theta = theta - alpha/m*x'*(x*theta - y); %梯度下降
% ============================================================
% Save the cost J in every iteration
J_history(iter) = computeCost(x, y, theta);
end
end
%% ============= Part 4: Visualizing J(theta_0, theta_1) =============
fprintf('Visualizing J(theta_0, theta_1) ...\n')
% Grid over which we will calculate J
theta0_vals = linspace(-10, 10, 100); %函式產生-10和10之間的100個等間距點的行向量
theta1_vals = linspace(-1, 4, 100); %%函式產生-1和4之間的100個等間距點的行向量
% initialize J_vals to a matrix of 0's (初始化J為0矩陣)
J_vals = zeros(length(theta0_vals), length(theta1_vals));
% Fill out J_vals
for i = 1:length(theta0_vals) %開始迭代i從1開始
for j = 1:length(theta1_vals)
t = [theta0_vals(i); theta1_vals(j)];
J_vals(i,j) = computeCost(x, y, t);
%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
end
end
% Because of the way meshgrids work in the surf command, we need to
% transpose J_vals before calling surf, or else the axes will be flipped
J_vals = J_vals';
% Surface plot (曲面圖)
figure;
surf(theta0_vals, theta1_vals, J_vals) %建立一個三維曲面圖。該函式將矩陣J中的值繪製為由X和Y定義的x-y平面中的網格上方的高度。
xlabel('\theta_0'); ylabel('\theta_1');
% Contour plot
figure;
% Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100
contour(theta0_vals, theta1_vals, J_vals, logspace(-2, 3, 20)) %繪製矩陣 Z 的等高線圖
xlabel('\theta_0'); ylabel('\theta_1');
hold on;
plot(theta(1), theta(2), 'rx', 'MarkerSize', 10, 'LineWidth', 2);