Python使用Matplotlib和Imagemagick实现感知器算法可视化与GIF导出

首先这类教程网上有很多基本的功能都可以实现，

1，安装ImageMagick

ImageMagick是一个类似于编码器的工具，下载地址：http://www.imagemagick.org/script/binary-releases.php 

2，   安装 PythonMagick，是ImageMagick的python开发包。

http://www.imagemagick.org/download/python/

3，安装FFmpeg

下载请到官网下载相应版本   的官方网址是 http://ffmpeg.mplayerhq.hu/。

安装之后配置好环境变量

参照http://blog.csdn.net/yjpeng125/article/details/70245685的代码    虽然遇到很多坑  最后还是实现了gif导出

import copy
from matplotlib import pyplot as plt
from matplotlib import animation
plt.rcParams ['animation.ffmpeg_path'] = r'C:\ffmpeg\bin\ffmpeg.exe'
 
training_set = [[(3, 3), 1], [(4, 3), 1], [(1, 1), -1]]
w = [0, 0]
b = 0
history = []
 
 
def update(item):
# 更新权重
    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])
    # you can uncomment this line to check the process of stochastic gradient descent
 
 
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 = 0
    for item in training_set:
        if cal(item) <= 0:
            flag = True
            update(item)
    # draw a graph to show the process
    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
 
    # first set up the figure, the axis, and the plot element we want to animate
    fig = plt.figure()
    ax = plt.axes()
    line, = ax.plot([], [], 'g', lw=3)
    label = ax.text(0,1,'')
   #label = ax.text(([]),([]),'', fontsize=16)
 
    # initialization function: plot the background of each frame
    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_, 'rx')
        plt.axis([-6, 6, -6, 6])
        plt.grid(True)
        plt.xlabel('x')
        plt.ylabel('y')
        plt.title('Perceptron Algorithm (Lizheng)')
        return line, label
 
    # animation function.  this is called sequentially
    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
        y1 = -(b + w[0] * x1) / w[1]
        x2 = 7
        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

1. anim.save('perceptron.gif', fps=2, writer='imagemagick')

# 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() 上面的代码是在前人的基础上做了修改后可以正常跑通的

提示

坑的位置

--------------------------1--------------------------------------------------------------------------------------------------------------------------------------------------------------------

这个地方如果大家没有更改的话 跑起来会有提示

-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

2