%COSTFUNCTION2 Summary ofthis function goes here % linear regression -> y=theta0 + theta1*x % parameter: x:m*n theta:n*1 y:m*1 (m=4,n=1) % %Data x=[1;2;3;4]; y=[1.1;2.2;2.7;3.8]; m=size(x,1); hypothesis = h_func(x,theta); delta = hypothesis - y; jVal=sum(...
% 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 particu...
plot(X(:,2), X*theta, '-') legend('Training data', 'Linear regression') hold off function theta = gradientDescent(X, y, theta, alpha, num_iters) m = length(y); % 样本数量 for iter = 1:num_iters H = X * theta; %(97,2)*(2*1)=(97,1) Sum = [0 ; 0]; %(2,1),...
MATLAB Workshop 15 - Linear Regression in MATLAB 线性回归分析
MATLAB code example and how to use the mvregress function to estimate the coefficients.) Applications of linear regression Linear regressions have some properties that make them very interesting for the following applications: Prediction or forecasting: Use a regression model to build a forecast model...
使用sklearn的LinearRegression进行简单线性回归 输出residual sum of squares,R-squared及模型的系数 画出拟合的曲线 ''' if X.ndim == 1: x = X.reshape(-1,1) else: x = X show_data(x, y, False) #start your code here model = LinearRegression().fit(x,y) ...
Ramesh (2011), A MATLABTM code to perform weighted linear regression with (correlated or uncorrelated) errors in bivariate data, J Geol Soc India, 77(4), 377-380.Thirumalai, K., Singh, A., and Ramesh, R.: A MATLAB Code to Perform Weighted Linear Regression with (correlated or uncor-...
legend('Training data', 'Linear regression') hold off function theta = gradientDescent(X, y, theta, alpha, num_iters) m = length(y); % 样本数量 for iter = 1:num_iters H = X * theta; %(97,2)*(2*1)=(97,1) Sum = [0 ; 0]; %(2,1),记录偏导,求和 ...
matlabCopy code%示例代码:解决线性回归问题functionmain()%生成示例数据X=[1,2,3,4,5]';%输入特征 y=[2.5,3.5,4.5,5.5,6.5]';%目标值%使用线性回归模型进行拟合 weights=linear_regression(X,y);%打印拟合结果fprintf('拟合结果:\n');fprintf(' 斜率: %.3f\n',weights(2));fprintf(' 截距: %.3f...
Please see the below code for your reference. 테마복사 Predictors = randn(567, 541); Response = randn(9, 541); for i = 0:8 % iterate over all data points validationdataX = Predictors(63*i+1:63*(i+1),:); validationdataY = Respo...