numpy实现adabosst算法

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))

numpy实现adabosst算法

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