def f(x):
    ans=w[0]*x[0]+w[1]*x[1]+b
    return ans
def perception(data_set):
    global b
    flag=True
    for data in data_set:
        if data[1]*f(data[0])<=0:
            w[0]+=eta*data[0][0]*data[1]
            w[1]+=eta*data[0][1]*data[1]
            b+=eta*data[1]
            flag=False
    if
 
 flag==False:
        perception(data_set)
data_set=[[[3,3],1],[[4,3],1],[[1,1],-1]]
eta=1
w=[0,0]
b=0
perception(data_set)
print(w,b)

別人的版本學習學習：

import copy
from matplotlib import pyplot as plt 
from matplotlib import animation
training_set=[[[3,3],1],[[4,3],1],[[1,1],-1]]
w=[0,0]
b=0
history=[]

def
 
 update(item):
   # 更新引數
   global w,b,history
   w[0]+=1*item[1]*item[0][0]
   w[1]+=1*item[1]*item[0][1]
   b+=1*item[1]
   print(w,b)
   history.append([copy.copy(w),b])

def cal(item):
    #計算函式值yi(w*x+b)
    res=0
    for i in range(len(item[0])):
        res+=item[0][i]*w[i]
    res+=b
    res*=item[1
 
]
    return res
def check():
    #檢查是否需要繼續更新
    flag=False
    for item in training_set:
        if cal(item)<=0:
            flag=True
            update(item)
    if not flag:
        print("RESULT:w:"+str(w)+"b:"+str(b))
    return flag
if __name__=="__main__":
    for i in range(1000):
        if not check():break
fig=plt.figure()
ax=plt.axes(xlim=(0,2),ylim=(-2,2))
line,=ax.plot([],[],'g',lw=2)
label=ax.text([],[],'')

def init():
    line.set_data([],[])
    x,y,x_,y_,=[],[],[],[]
    for p in training_set:
        if p[1]>0:
            x.append(p[0][0])
            y.append(p[0][1])
        else:
            x_.append(p[0][0])
            y_.append(p[0][1])
    plt.plot(x,y,'bo',x_,y_,'r^')
    plt.axis([-6,6,-6,6])
    plt.grid()
    plt.xlabel('x1')
    plt.ylabel('x2')
    plt.title('Perception')
    return line,label
def animate(i):
    global history,ax,line,label
    w=history[i][0]
    b=history[i][1]
    if w[1]==0:return line,label
    x1 = -7.0
    y1 = -(b + w[0] * x1) / w[1]
    x2 = 7.0
    y2 = -(b + w[0] * x2) / w[1]
    line.set_data([x1, x2], [y1, y2])
    x1 = 0.0
    y1 = -(b + w[0] * x1) / w[1]
    label.set_text(str(history[i][0]) + ' ' + str(b))
    label.set_position([x1, y1])
    return line, label

# call the animator.  blit=true means only re-draw the parts that have changed.
anim = animation.FuncAnimation(fig, animate, init_func=init, frames=len(history), interval=1000, repeat=True,
                                blit=True)
plt.show()