11-20201013 感知机5-实例1

题目.jpg

http://www.hankcs.com/ml/the-perceptron.html

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):
    """
    update parameters using stochastic gradient descent
    :param item:an item which is classified into wrong class
    :return: nothing
    """
    
    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):
    """
    calculate the functional distance between 'item' an the dicision surface. output yi(w*xi +b)
    :param item:
    :return:
    """
    res = 0
    for i in range(len(item[0])):
        res += item[0][i]*w[i]
    res +=b
    res *= item[1]
    
    return res

def check():
    """
    check if the hyperplane can classify the examples correctly
    :return: true if it can
    """
    flag = False
    for item in training_set:
        if cal(item) <=0:      # 只计算分错的点
            flag = True        # 当存在分错的点
            update(item)
    if not flag:
        print("Result: w:"+str(w)+" b: " +str(b))
    return flag

for i in range(1000):
    if not check():break

[3, 3] 1
[2, 2] 0
[1, 1] -1
[0, 0] -2
[3, 3] -1
[2, 2] -2
[1, 1] -3
Result: w:[1, 1] b: -3

# first set upp the figure, the axis, and the plot element we want to animate
fig = plt.figure()
ax = plt.axes(xlim=(0, 2), ylim=(-2, 2))
line, = ax.plot([], [], 'g', lw=2)
label = ax.text(0.5, 0.9, '', transform=ax.transAxes)

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][1])
            y_.append(p[0][1])
    
    plt.plot(x,y,'bo',x_,y_,'rx')
    plt.axis([-6,6,-6,6])
    plt.grid(True)
    plt.xlabel('x')
    plt.ylabel('y')
    plt.title('Perceptton algorithm')

    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 = -2
    y1 = -(b + w[0] * x1) / w[1]
    x2 = 2  
    y2 = -(b + w[0] * x2) / w[1]
    line.set_data([x1, x2], [y1, y2])

    x1 = 0
    y1 = -(b + w[0] * x1) / w[1]
    label.set_text(history[i])
    label.set_position([x1, y1])
    
    return line, label
 
# call the animator.  blit=true means only re-draw the parts that have changed.
print(history)
anim = animation.FuncAnimation(fig, animate, init_func=init, frames=len(history), interval=1000, repeat=True,blit=True)

plt.show()
anim.save('perceptron.gif', fps=2, writer='pillow')
    

[[[3, 3], 1], [[2, 2], 0], [[1, 1], -1], [[0, 0], -2], [[3, 3], -1], [[2, 2], -2], [[1, 1], -3]]

perceptron.gif