1function [ jVal,gradient ] =costFunction2( theta )2%COSTFUNCTION2 Summary ofthisfunction goes here3% linear regression -> y=theta0 + theta1*x4% parameter: x:m*n theta:n*1y:m*1(m=4,n=1)5%67%Data8x=[1;2;3;4];9y=[1.1;2.2;2.7;3.8];10m=size(x,1);1112hypothesis =h_func(...
第二步是检验了Loss function的正确性,也就是 computeCost函数 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 ...
can anyone tell me an accurate function for linear regression (fitting a line to data). I am also interested in the slop, interception and R-square of the fitted line. I am only familiar with polifit Thanks Aziz 댓글 수: 0
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 ...
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 ...
MATLAB Workshop 15 - Linear Regression in MATLAB 线性回归分析
(See 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 ...
然后用LinearRegression求多项式则需要用多项式把 变成 这种形式替换成 就行了 AI检测代码解析 from os import path import numpy as np import scipy as sp import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.preprocessing import PolynomialFeatures ...
下面介绍Linear Regression Model,这是一个线性回归模型,也可以称之为代价函数以及平方差函数 在使用这个模型之前,我们必须要先假设一个函数,也就是上式中的ℎ_?,但是ℎ_?是一个带有参数的不确定函数,而学习的算法的作用的就正是通过机器学习让计算机通过学习算法自己去寻找最能拟合数据样本的参数,从而找出这个最...
MATLAB Workshop 15 - Linear Regression in MATLAB 线性回归分析