机器学习基石 PLA 作业1

本文主要是介绍机器学习基石 PLA 作业1，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

主要参考：
https://blog.csdn.net/sjz_hahalala479/article/details/81003517
https://blog.csdn.net/qq_25037903/article/details/83861118
小菜鸡开始学习ML了。。。
15.

import numpy as np
def getDataset(l,s):X = np.zeros((l,5)) #多一个x0Y = np.zeros((l,1))for i in range(l):ss = s[i].strip().split()#print (type(ss[0]))X[i][0] = 1X[i][1] = ss[0]X[i][2] = ss[1]X[i][3] = ss[2]X[i][4] = ss[3]Y[i][0] = ss[4]return X,Y
def sign(X,W):w = np.matrix(W)x = np.matrix(X)if np.dot(W,X) >0:#print (W)return 1else:return -1
def pla(l,s,W):X,Y = getDataset(l,s)iteration = 1updates = 0while(iteration!=0):iteration = 0for i in range(l):if sign(X[i],W) != Y[i]:W = W+Y[i]*X[i]iteration +=1#break #这里不是break 之前搞错了算法流程 一直不对 是所有点顺序遍历后重新迭代updates += iterationreturn updateswith open('result.txt','r') as f:s = f.readlines()
l = len(s) #行
W = np.zeros(5)
print(pla(l,s,W))

16、17

import numpy as np
import random
def getDataset(l,s):X = np.zeros((l,5)) #多一个x0Y = np.zeros((l,1))for i in range(l):ss = s[i].strip().split()#print (type(ss[0]))X[i][0] = 1X[i][1] = ss[0]X[i][2] = ss[1]X[i][3] = ss[2]X[i][4] = ss[3]Y[i][0] = ss[4]return X,Y
def sign(X,W):w = np.matrix(W)x = np.matrix(X)if np.dot(W,X) >0:#print (W)return 1else:return -1
def pla(l,s,W,randomlist):X,Y = getDataset(l,s)iteration = 1updates = 0while(iteration!=0):iteration = 0random.shuffle(randomlist)for i in randomlist:if sign(X[i],W) != Y[i]:W = W+Y[i]*X[i]*0.25iteration +=1#breakupdates += iterationreturn updatesif __name__=='__main__':with open('result.txt','r') as f:s = f.readlines()l = len(s) #行数m=[]rl = list(range(l))for i in range(2000):W = np.zeros(5)m.append(pla(l,s,W,rl))mm = np.array(m)print(np.mean(mm))

18、19、20

import numpy as np
import random
def getDataset(l,s):X = np.zeros((l,5)) #多一个x0=1Y = np.zeros((l,1))for i in range(l):ss = s[i].strip().split()#print (type(ss[0]))X[i][0] = 1X[i][1] = ss[0]X[i][2] = ss[1]X[i][3] = ss[2]X[i][4] = ss[3]Y[i][0] = ss[4]return X,Y
def sign(X,W):if np.dot(W,X) >0:return 1else:return -1
def mistake(W,X,Y):cnt = 0ls= len(X)for i in range(ls):res = sign(X[i],W)if res!=Y[i]:cnt +=1return cnt/ls
def pla(l,s,W,randomlist):X,Y = getDataset(l,s)Wt = Wupdates = 100while(updates>0):iteration = 0random.shuffle(randomlist)for i in randomlist:if sign(X[i],Wt) != Y[i]:  #这里比较的是Wt 就是一直更新的线 而不是最好的线 最好的W是在不断更新中保存的 Wt = Wt+Y[i]*X[i]#print(Wt,W)miscnt1= mistake(Wt,X,Y)  #现有线向量的错误率miscnt2 = mistake(W,X,Y)  #原有线向量的错误率updates -= 1if miscnt1<miscnt2:W = Wtif updates==0:breakif updates ==0:breakreturn Wif __name__=='__main__':with open('result.txt','r') as f:s = f.readlines()with open('mytest.txt','r') as ff:ss =ff.readlines()l = len(s) #行数ll = len(ss)tstX, tstY = getDataset(ll, ss)m =[]rl = list(range(l)) #l索引listfor i in range(20):W = np.zeros(5)Wm = pla(l,s,W,rl)m.append(mistake(Wm,tstX,tstY))a = np.array(m)print(a.mean())