import numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import classification_reportdensity=np.array([0.697,0.774,0.634,0.608,0.556,0.430,0.481,0.437,0.666,0.243,0.245,0.343,0.639,0.657,0.360,0.593,0.719]).reshape(-1,1)sugar_rate=np.array([0.460,0.376,0.264,0.318,0.215,0.237,0.149,0.211,0.091,0.267,0.057,0.099,0.161,0.198,0.370,0.042,0.103]).reshape(-1,1)xtrain=np.hstack((density,sugar_rate))xtrain=np.hstack((np.ones([density.shape[0],1]),xtrain))ytrain=np.array([1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0]).reshape(-1,1)xtrain,xtest,ytrain,ytest=train_test_split(xtrain,ytrain,test_size=0.25,random_state=33)def sigmoid(z): return 1/(1+np.exp(-z))#print(sigmoid(density))def logit_regression(theta,x,y,iteration=100,learning_rate=0.1,lbd=0.01): for i in range(iteration): theta=theta-learning_rate/y.shape[0]*(np.dot(x.transpose(),(sigmoid(np.dot(x,theta))-y))+lbd*theta) cost=-1/y.shape[0]*(np.dot(y.transpose(),np.log(sigmoid(np.dot(x,theta))))+np.dot((1-y).transpose(),np.log(1-sigmoid(np.dot(x,theta)))))+lbd/(2*y.shape[0])*np.dot(theta.transpose(),theta) print('---------Iteration %d,cost is %f-------------'%(i,cost)) return thetadef predict(theta,x): pre=np.zeros([x.shape[0],1]) for idx,valu in enumerate(np.dot(x,theta)): if sigmoid(valu)>=0.5: pre[idx]=1 else: pre[idx]=0 return pre theta_init=np.random.rand(3,1)theta=logit_regression(theta_init,xtrain,ytrain,learning_rate=1)pre=predict(theta,xtest)print('predictions are',pre)print('ground truth is',ytest)print('theta is ',theta)print('the accuracy is',np.mean(pre==ytest))print(classification_report(ytest,pre,target_names=['Bad','Good']))