假设函数 多元线性回归(multivariate linear regression) 将线性组合转换成矩阵乘法计算 问题描述 有n+1个特征量的gradient descent 特征缩放(feature scaling) 保证多个特征在相似范围内,这样梯度下降法能够更快的收敛 此时代价函数J的等值线图是椭圆形,梯度下降要来回波动,最终才收敛到最小值。 采用特征缩放 除以最大...
Using ten-year car sales data, this research proposes a machine learning approach using gradient descent (GD) to fitting multiple linear regression for Thailand car sales forecasts. The resulted forecasting accuracy is then compared with that of a normal equation method (NE) as well as that ...
1.Linear Regression with Multiple Variables(多变量线性回归) 1.1多维特征(Multiple features) 前面都是单变量的回归模型,通过对模型增加更多的特征,就可以构成一个含有多个变量的模型,模型中的特征为(x1,x2,...,xn)。 以房价举例,前面在单变量的学习中只是用到了房屋的尺寸作为x来预测房价y,现在可以增加房间数...
Linear Regression 导读 Machine Learning (二) :Linear Regression & Loss Function & Gradient Descent Compared with most people are familiar with linear models, in this article, I will share my unde... day3 Linear Classification 接下来我们讨论线性分类,线性分类非常重要,同时他也是一个非常简单的算法,...
ing gradient descent multiple times with a `hold on' command between plots. Concretely, if you've tried three di erent values of alpha (you should probably try more values than this) and stored the costs in J1, J2 and J3, you can use the following commands to plot them on the same ...
Linear regression with multiple variables(多特征的线型回归)算法实例_梯度下降解法(Gradient DesentMulti)以及正规方程解法(Normal Equation),%第一列为sizeofHouse(feet^2),第二列为numberofbedroom,第三列为priceofHouse12104,3,39990021600,3,32990032400,3,3690004
1))m=y.shape[0]x,mu,sigma=featureNormalize(x)X=np.hstack([x,np.ones((x.shape[0],1))])#X=X[range(2),:]# y=y[range(2),:]theta=np.zeros((3,1))j=computeCost(X,y,theta)J_history,theta=gradientDescent(X,y,theta,alpha,iterations)print('Theta found by gradient descent',...
一、Linear Regression with multiple variables (多元线性回归): 二、Gradient descent for multiple variables(多元梯度下降法) (1)Gradient descent for multiple variables 偏导数项展开: (2)Feature Scaling(特征缩放) 原因:若特征规模差别很大(如x1:0-2000,x2:1-5),得到的代价函数可能会不光滑,导致梯度下降收...
4 多变量线性回归(Linear Regression with Multiple Variables)4.1 多特征(Multiple Features)4.2 多变量梯度下降(Gradient Descent for Multiple Variables)4.3 梯度下降实践1-特征值缩放(Gradient Descent in Practice I - Feature Scaling)4.4 梯度下降实践2-学习速率(Gradient Descent in Practice II - Learning Rate...
def gradient_descent(stepSize, x, y, tolerance=0.000000001, max_iter=100000): """梯度下降""" iter = 0 # initial theta thetas = [0, 0]; # Iterate Loop while True: gradient = sum_of_gradient(x, y, thetas); next_thetas = step(thetas, gradient, stepSize); ...