print("岭回归的系数(已经缩放至原尺寸):",unStandCoff)
print("岭回归的截距(已经缩放至原尺寸):",-1*sum(multiply(unStandCoff,xMean))+yMean)
print('岭回归RSS:', LoadData.rssError(Y, YHat))
if __name__ == '__main__':
lgX = []
lgY = []
setDataCollect(lgX, lgY)
lgX = mat(lgX)
lgY = mat(lgY).T
m = lgX.shape[0]
n = lgX.shape[1]
lgX1 = LoadData.addAllOneColumn(lgX)
print('属性个数:', n)
print('训练实例个数:', m)
set_printoptions(suppress=True)
print('属性矩阵:', lgX)
print('类标签矩阵矩阵:', lgY.T)
theta = regression.standRegres(lgX1, lgY) # theta 是n*1 训练数据直接当测试数据
print('线性回归系数为:', theta)
YHat = lgX1 * theta # YHat 是 m*1
# for i in range(m):
# print('真实值:', lgY[i], '预测值:', YHat[i])
print('标准线性回归RSS:', LoadData.rssError(lgY, YHat))
crossValidationRidgeRegression(lgX, lgY)
import LoadData
import ForwardStagewiseR
import regression
import matplotlib.pyplot as plt
from numpy import *
X,Y,attNum,trainingSampleNum=LoadData.loadDataSet('abalone.txt')
xStd=std(X,0) #得到标准化前X的标准差
yMean=mean(Y,0) #得到中心化前的Y的均值
XStand,YCentered=LoadData.standardize(X),LoadData.centered(Y)
numIt=5000
allWS=ForwardStagewiseR.forwardStagewiseR(XStand,YCentered,0.005,numIt)
YHat1=XStand*(mat(allWS[numIt-1]).T)+yMean
allWS=allWS/xStd
print("前向逐步回归系数最后一次迭代系数为:",allWS[numIt-1])
print("前向逐步回归的Error为",LoadData.rssError(Y,YHat1))
xOne=LoadData.addAllOneColumn(X)
thetaStd=regression.standRegres(xOne, Y) # theta 是n*1 训练数据直接当测试数据
print('线性回归系数为:',thetaStd.T)
YHat2=xOne*thetaStd
print("标准线性回归的Error为",LoadData.rssError(Y,YHat2))
plt.plot(range(numIt),allWS)
plt.show()