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
4000
])
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
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

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这个地方如果大家没有更改的话 跑起来会有提示


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