实验报告2,3线性回归逻辑回归.docxVIP

专业班级： 2018164 学号： 201816406 姓名：张锦渊 实验二：线性回归 实验代码： import numpy as np import matplotlib.pyplot as plt a = np.loadtxt('E:\机器学习实验二\ex1data1.txt') m=a.shape[0] print(m) print(type(a)) x=a[:,0] y=a[:,1] plt.scatter(x,y,marker='*',color='r',s=20) theta0=0 theta1=0 iterations = 1500 alpha = 0.01 def gradientdescent(x,y,theta0,theta1,iterations,alpha): J_h=np.zeros( (iterations,1) ) for i in range(0,iterations): y_hat=theta0+theta1*x temp0=theta0-alpha*((1/m)*sum(y_hat-y)) temp1=theta1-alpha*(1/m)*sum((y_hat-y)*x) theta0=temp0 theta1=temp1 y_hat2=theta0+theta1*x aa=sum((y_hat2-y)**2) J=aa*(1/(2*m)) J_h[i,:]=J return theta0,theta1,J_h (theta0,theta1,J_h) = gradientdescent(x,y,theta0,theta1,iterations,alpha) print(theta1) print(theta0) plt.plot(x,theta0+theta1*x) plt.title("fittingcurve") plt.show() x2=np.arange(iterations) plt.plot(x2,J_h) plt.title("costfunction") plt.show() 实验结果： 多变量线性回归 import?numpy?as?npimport?pandas?as?pdimport?matplotlibimport?matplotlib.pyplot?as?pltmatplotlib.rcParams['font.sans-serif']=['SimHei']path="./ex1data2.txt"data=pd.read_csv(path,?header=None,?names=['size','num','price'])data.head()X=?data.loc[:,?['size',?'num']]y=?data.loc[:,?'price']X=?(X?-?X.mean())?/?X.std()X.insert(0,?'ones',?1)X.head()X=np.matrix(X.values)y=np.matrix(y.values)theta=(np.matrix([0,0,0])).Tdef?computeCost(x,y,theta):????inner=np.sum(np.power((theta.T*x.T-y),2))????return?inner/(2*len(x))def?gradientDescent(x,y,theta,alpha,iters):????temp=np.matrix(np.zeros(theta.shape))????cost=np.zeros(iters)????for?i?in?range(iters):????????temp=theta-(alpha/len(x))*((theta.T*x.T-y)*x).T????????theta=temp????????cost[i]=computeCost(x,y,theta)????return?theta,costalpha=[0.0001,0.001,0.01,0.0003,0.003,0.03]iters=2000x=np.arange(iters)fig,ax=plt