Matplotlib 动画未显示(梯度下降测试)

Question

我试图创建一个matplotlib动画来练习使用梯度下降来进行线性回归。但是我无法让动画工作。

我设法通过使用anim.show()使动画工作，但这导致了AttributeError，因为动画类没有方法。不知道为什么这实际上会导致动画工作

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.animation as animation

def main():
# Initialize Dataset
X = 10*np.random.rand(50)
y = 8*X + 1 + 2.5*np.random.randn(50)
model = LinearRegression()
model.train(X,y)
model.animate(X,y)
class LinearRegression():
# Using Gradient Descent for Linear Regression
def __init__(self, learning_rate=0.001, epochs=100):
self.learning_rate = learning_rate
self.epochs = epochs
self.a_0 = 0
self.a_1 = 0
self.w_list = []
def train(self, X, y):
n = X.shape[0]
for i in range(self.epochs):
self.w_list.append([self.a_0,self.a_1])
y_train = self.a_0 + self.a_1 * X
error = y - y_train        # Whether you use y_train - y or y - y_train will make a difference
mse = np.sum(error ** 2) / n
self.a_0 -= -2/n * np.sum(error) * self.learning_rate
self.a_1 -= -2/n * np.sum(error * X) * self.learning_rate
#if i%10 == 0:
#   print("MSE",str(i)+":", mse)
self.w_list = np.array(self.w_list)
def animate(self, X, y):
fig, ax = plt.subplots()
ax.scatter(X,y)
plot_range = np.array(range(int(min(X))-1,int(max(X))+3))
a_0,a_1 = self.w_list[0,]
y_plot = plot_range*a_1 + a_0
ln, = ax.plot(plot_range, y_plot, color="red", label="Best Fit")
def animator(frame):
a_0, a_1 = self.w_list[frame,]
y_plot = plot_range * a_1 + a_0
ln.set_data(plot_range,y_plot)

print("Launching Animation")
anim = animation.FuncAnimation(fig,func = animator, frames = self.epochs)
anim.show()
if __name__ == "__main__":
main()

Answer 1

您需要调用plt.show()才能打开绘图窗口。

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.animation as animation

def main():
# Initialize Dataset
X = 10*np.random.rand(50)
y = 8*X + 1 + 2.5*np.random.randn(50)
model = LinearRegression()
model.train(X,y)
model.animate(X,y)
class LinearRegression():
# Using Gradient Descent for Linear Regression
def __init__(self, learning_rate=0.001, epochs=100):
self.learning_rate = learning_rate
self.epochs = epochs
self.a_0 = 0
self.a_1 = 0
self.w_list = []
def train(self, X, y):
n = X.shape[0]
for i in range(self.epochs):
self.w_list.append([self.a_0,self.a_1])
y_train = self.a_0 + self.a_1 * X
error = y - y_train        # Whether you use y_train - y or y - y_train will make a difference
mse = np.sum(error ** 2) / n
self.a_0 -= -2/n * np.sum(error) * self.learning_rate
self.a_1 -= -2/n * np.sum(error * X) * self.learning_rate
#if i%10 == 0:
#   print("MSE",str(i)+":", mse)
self.w_list = np.array(self.w_list)
def animate(self, X, y):
fig, ax = plt.subplots()
ax.scatter(X,y)
plot_range = np.array(range(int(min(X))-1,int(max(X))+3))
a_0,a_1 = self.w_list[0,]
y_plot = plot_range*a_1 + a_0
ln, = ax.plot(plot_range, y_plot, color="red", label="Best Fit")
def animator(frame):
a_0, a_1 = self.w_list[frame,]
y_plot = plot_range * a_1 + a_0
ln.set_data(plot_range,y_plot)

print("Launching Animation")
anim = animation.FuncAnimation(fig,func = animator, frames = self.epochs)
plt.show()
if __name__ == "__main__":
main()