一元线性回归(Simple Linear Regression): 假设只有一个自变量x(independent variable,也可称为输入input, 特征feature),其与因变量y(dependent variable,也可称为响应response, 目标target)之间呈线性关系,当然x和y之间不会完全是直线关系,而是会有一些波动(因为在现实中,不一定只有一个自变量x会影响因变量y,可能还会...
25 plot(x(:,2), x*theta, '-')%这个就是回归曲线的那个图 26 legend('Training data', 'Linear regression')%标出图像中各曲线标志所代表的意义,就是每个数据点表示成的圆圈或线段所代表 %的意义 27 hold off % don't overlay any more plots on this figure,指关掉前面的那幅图 28 % Closed form...
temp=x*theta'-y;sqrerrors=temp.^2;theta=theta-learning_rate*(1/m)*(temp'*x);Jcost(step)=(1/2*m)*sum(sqrerrors);disp(step),disp(Jcost(step))end figure;plot(Jcost)title('The relation between J and iteration ');ylabel('J')xlabel('iteration')legend('\alpha = 0.07')figureplot(...
tools for machine learning ; experience is important 2.supervised learning “right answers”given supervised learning:数据集中的每个数据都是正确的答案 Regression Question : predict continuous valued output (Regression Question) key : predict ;continuous data;回归问题 Classification Problem: discrete va...
% 实际数据标签theta=theta-learning_rate*1/m*(X'*error);% 特征转置变成行左,误差列右,加速手动求和运算cur_cost=costf(X,y,theta);% 计算进行一次学习后的代价ifcur_cost-prev_cost>0% 学习率过大,中断学习并返回theta=zeros(size(theta));disp('Learning rate is too large, break!');break;end...
{'linear regression','data','prediction'})2.正规方程法:1.x=load('ex2x.dat');2.y=load('ex2y.dat');3.m=length(x)4.x=[ones(m,1),x];5.theta=inv(x'*x)*x'*y6.figure7.plot(x(:,2),y,'o');8.%plot3(x(:,2),x*theta,'-g','Linewidth',2);9.p1=plot3(x(:,2),...
In Machine Learning, predicting the future is very important. How Does it Work? Python has methods for finding a relationship between data-points and to draw a line of linear regression. We will show you how to use these methods instead of going through the mathematic formula. ...
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Machine Learning Notes-Linear Regression-Udacity 什么是 Regression? Regression 就是想找到因变量和自变量之间的关系,用一个函数来表示,并且可用这个函数来预测任意一个新的 x 会有怎样的 y 。 那么怎么找最好的那条线来表示 x 与 y 之间的关系呢?
Machine Learning #Lab1# Linear Regression Machine Learning Lab1 打算把Andrew Ng教授的#Machine Learning#相关的6个实验一一实现了贴出来～ 预计时间长度战线会拉的比較长(毕竟JOS的7级浮屠还没搞定.) --- 实验内容: 线性拟合 ---