Changing colour of regression line in a scatter plot

Question

Danny 2023-12-27

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2064342-changing-colour-of-regression-line-in-a-scatter-plot

编辑： Dyuman Joshi 2023-12-27

I am trying to either change the colour of the line in my scatter graph linear regression graph or the colour of the plots to make it easier to read

This is my current code to try and change the line colour to blue except both the line and points stay in red each time?

plot(x, y, 'rx', 'MarkerSize', 2, "MarkerFaceColor","b");

ylabel('Crash Risk');

xlabel('Ln Market Value');

title('How Ln Market Value affects Crash Risk')

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Danny 2023-12-27

编辑：Adam Danz 2023-12-27

在 MATLAB Online 中打开

The task is to plot a graph showing how the lnmarket value of shares affects share price crash risk. There is too much data to share unfortunately but hope that helps for now

This is the full code i'm using

%% Initialization
clear all; close all; clc
%% ======================= Part 1: Plotting =======================
fprintf('Plotting Data ...\n')
data = load('LnMarketVal.txt');
X = data(:, 1); y = data(:, 2);
m = length(y); % number of training examples
% Plot Data
% Note: You have to complete the code in plotData.m
plotData(X, y);
fprintf('Program paused. Press enter to continue.\n');
pause;
%% =================== Part 2: Gradient descent ===================
fprintf('Running Gradient Descent ...\n')
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;
% compute and display initial cost
computeCost(X, y, theta)
% run gradient descent
theta = gradientDescent(X, y, theta, alpha, iterations);
% print theta to screen
fprintf('Theta found by gradient descent: ');
fprintf('%f %f \n', theta(1), theta(2));
% Plot the linear fit
hold on; % keep previous plot visible
plot(X(:,2), 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('Program paused. Press enter to continue.\n');
pause;
%% ============= Part 3: 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);
theta1_vals = linspace(-1, 4, 100);
% initialize J_vals to a matrix of 0's
J_vals = zeros(length(theta0_vals), length(theta1_vals));
% Fill out J_vals
for i = 1:length(theta0_vals)
    for j = 1:length(theta1_vals)
  	  t = [theta0_vals(i); theta1_vals(j)];    
      J_vals(i,j) = computeCost(X, y, t);
    end
end
J_vals = J_vals';
% Surface plot
figure;
surf(theta0_vals, theta1_vals, J_vals)
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))
xlabel('\theta_0'); ylabel('\theta_1');
hold on;
plot(theta(1), theta(2), 'rx', 'MarkerSize', 10, 'LineWidth', 2);

Star Strider 2023-12-27

在 MATLAB Online 中打开

LnMarketVal.txt

Missing function definitions.

%% Initialization
clear all; close all; clc
%% ======================= Part 1: Plotting =======================
fprintf('Plotting Data ...\n')
Plotting Data ...
data = load('LnMarketVal.txt');
X = data(:, 1); y = data(:, 2);
m = length(y); % number of training examples
% Plot Data
% Note: You have to complete the code in plotData.m
plotData(X, y);
Unrecognized function or variable 'plotData'.
% fprintf('Program paused. Press enter to continue.\n');
% pause;
%% =================== Part 2: Gradient descent ===================
fprintf('Running Gradient Descent ...\n')
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;
% compute and display initial cost
computeCost(X, y, theta)
% run gradient descent
theta = gradientDescent(X, y, theta, alpha, iterations);
% print theta to screen
fprintf('Theta found by gradient descent: ');
fprintf('%f %f \n', theta(1), theta(2));
% Plot the linear fit
hold on; % keep previous plot visible
plot(X(:,2), 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('Program paused. Press enter to continue.\n');
pause;
%% ============= Part 3: 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);
theta1_vals = linspace(-1, 4, 100);
% initialize J_vals to a matrix of 0's
J_vals = zeros(length(theta0_vals), length(theta1_vals));
% Fill out J_vals
for i = 1:length(theta0_vals)
    for j = 1:length(theta1_vals)
  	  t = [theta0_vals(i); theta1_vals(j)];    
      J_vals(i,j) = computeCost(X, y, t);
    end
end
J_vals = J_vals';
% Surface plot
figure;
surf(theta0_vals, theta1_vals, J_vals)
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))
xlabel('\theta_0'); ylabel('\theta_1');
hold on;
plot(theta(1), theta(2), 'rx', 'MarkerSize', 10, 'LineWidth', 2);