numpy实现adabosst算法

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import numpy as np  import matplotlib.pyplot as plt  class node:     def __init__(self,index,value,clas):         self.index=index         self.value=value         self.clas=clas def cpuloss(th,index):     sum=0     sum1=0     for i in range(len(train)):         if train[i,index]<=train[th,index]:             if train_lable[i]!=1:                 sum=sum+w1[i]         else:             if train_lable[i]!=-1:                 sum=sum+w1[i]       for i in range(len(train)):         if train[i,index]<=train[th,index]:             if train_lable[i]!=-1:                 sum1=sum1+w1[i]         else:             if train_lable[i]!=1:                 sum1=sum1+w1[i]     if sum>sum1:         return sum1,index,train[th,index],-1     else:         return sum,index,train[th,index],1           def create():     min=1     for i in range(4):         for j in range(len(train)):             k,k1,k2,k3=cpuloss(j,i)             if k<min:                 min=k                 index=k1                 value=k2                 cla=k3     no=node(index,value,cla)     return no def comerror(node):     e=0     for i in range(len(train)):         if train[i,node.index]<=node.value:             if train_lable[i]!=node.clas:                 e=e+w1[i]         else:             if node.clas==1:                 if train_lable[i]!=-1:                     e=e+w1[i]             else:                 if train_lable[i]!=1:                     e=e+w1[i]               return e def predict(th,node):     if train[th,node.index]<=node.value:         return node.clas     else:         if node.clas==1:             return -1         else:             return 1 def predict1(th,node):     if test[th,node.index]<=node.value:         return node.clas     else:         if node.clas==1:             return -1         else:             return 1 w=300 train=np.random.randint(-300,300,(w,4)) train=train.astype(float) train_lable=np.zeros((w,1)) test=np.random.randint(-300,300,(w,4)) traint=train.astype(float) test_lable=np.zeros((w,1)) for i in range(w):     if 1*train[i,0]+2*train[i,1]+3*train[i,2]+4*train[i,3]>0:         train_lable[i]=1     else:         train_lable[i]=-1     if 1*test[i,0]+2*test[i,1]+3*test[i,2]+4*test[i,3]>0:              test_lable[i]=1     else:         test_lable[i]=-1   for i in range(w):     if(train_lable[i]==0):         print(1)     if(test_lable[i]==0):         print(1) tree1=[] a=[] w1=np.zeros(len(train)) w1=w1+1/len(train) loss=1 while 1:     if loss==0:         break     no=create()     tree1.append(no)     e=comerror(no)     a.append(np.log((1-e)/e)/2)     z=0     for i in range(len(train)):         z=z+w1[i]*np.exp(-a[len(a)-1]*train_lable[i]*predict(i,tree1[len(tree1)-1]))     for i in range(len(train)):         w1[i]=w1[i]*np.exp(-a[len(a)-1]*train_lable[i]*predict(i,tree1[len(tree1)-1]))/z     loss=0     for i in range(len(train)):         sum=0         for j in range(len(tree1)):             sum=sum+a[j]*predict(i,tree1[j])         if sum>0 and train_lable[i]!=1:             loss+=1         elif sum<0 and train_lable[i]!=-1:             loss+=1        print(loss) acc=0 for i in range(len(test)):     sum=0     for j in range(len(tree1)):         sum=sum+a[j]*predict1(i,tree1[j])     if sum>0 and test_lable[i]==1:         acc+=1     elif sum<0 and test_lable[i]==-1:         acc+=1   print(acc/len(test))

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