一:感知机算法原始形式实现
（一）伪代码
（二）实现感知机算法
class MyPerceptron:
def __init__(self): # 属性初始化
self.w = None
self.b = 0
self.l_rate = 1
def fit(self, X_train, y_train):
global history_w, history_b #保持w,b信息，方便一会绘制图像
# 根据X形状,设置w
self.w = np.zeros(X_train.shape[1])
i = 0
while i < X_train.shape[0]: # 注意我们按顺序查看误分类点
X = X_train[i]
y = y_train[i]
# 如果y*(wX+b)<=0,则是误分类点,我们就要更新一次w,b,我们每更新一次w,b,我们就要从新查找整个数据集
if y * (np.dot(self.w, X) + self.b) <= 0:
self.w = self.w + self.l_rate * np.dot(y, X)
self.b = self.b + self.l_rate * y
i = 0
history_w.append(self.w)
history_b.append(self.b)
else:
i += 1
（三）设置数据，进行训练
if __name__ == "__main__":
# 构建数据集和标签值
X_train = np.array([[3, 3], [4, 3], [1, 1]])
y = np.array([1, 1, -1])
history_w = []
history_b = []
perc = MyPerceptron()
perc.fit(X_train, y) # 进行训练 获取w,b信息
（四）数据可视化
# 数据集可视化
fig = plt.figure()
ax = plt.axes()
line, = ax.plot([], [], 'g', lw=2)
def init():
line.set_data([], [])
plt.scatter(X_train[np.where(y == 1), 0], X_train[np.where(y == 1), 1], marker="o", c="b")
plt.scatter(X_train[np.where(y == -1), 0], X_train[np.where(y == -1), 1], marker="x", c="r")
return line,
def update(i):
global history_w, history_b, ax, line
w = history_w[i]
b = history_b[i]
if w[1] == 0:
return line,
x1 = -1
y1 = -(b + w[0] * x1) / w[1]
x2 = 6
y2 = -(b + w[0] * x2) / w[1]
line.set_data([x1, x2], [y1, y2])
return line,
plt.xlim(-1, 6)
plt.ylim(-1, 4)
print(history_w)
print(history_b)
#[[[3, 3], 1], [[2, 2], 0], [[1, 1], -1], [[0, 0], -2], [[3, 3], -1], [[2, 2], -2], [[1, 1], -3]]
ani = anim.FuncAnimation(fig=fig, func=update,init_func=init, frames=len(history_b), interval=1000, repeat=True, blit=True)
plt.show()
参数详解：
-
fig 进行动画绘制的figure
-
func 自定义动画函数，即传入刚定义的函数animate
-
frames 动画长度，一次循环包含的帧数
-
init_func 自定义开始帧，即传入刚定义的函数init
-
interval 更新频率，以ms计
-
blit 选择更新所有点，还是仅更新产生变化的点。应选择True，但mac用户请选择False，否则无法显示动画
注意：我们要实现Animation动画，需要设置pycharm中(File->Settings->Tools->Python Scientific）的Show plots in tool window选项（disable不使用）
（五）结果显示
![]()
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as anim
class MyPerceptron:
def __init__(self): # 属性初始化
self.w = None
self.b = 0
self.l_rate = 1
def fit(self, X_train, y_train):
global history_w, history_b
# 根据X形状,设置w
self.w = np.zeros(X_train.shape[1])
i = 0
while i < X_train.shape[0]: # 注意我们按顺序查看误分类点
X = X_train[i]
y = y_train[i]
# 如果y*(wX+b)<=0,则是误分类点,我们就要更新一次w,b,我们每更新一次w,b,我们就要从新查找整个数据集
if y * (np.dot(self.w, X) + self.b) <= 0:
self.w = self.w + self.l_rate * np.dot(y, X)
self.b = self.b + self.l_rate * y
i = 0
history_w.append(self.w)
history_b.append(self.b)
else:
i += 1
if __name__ == "__main__":
# 构建数据集和标签值
X_train = np.array([[3, 3], [4, 3], [1, 1]])
y = np.array([1, 1, -1])
history_w = []
history_b = []
perc = MyPerceptron()
perc.fit(X_train, y) # 进行训练 获取w,b信息
# 数据集可视化
fig = plt.figure()
ax = plt.axes()
line, = ax.plot([], [], 'g', lw=2)
def init():
line.set_data([], [])
plt.scatter(X_train[np.where(y == 1), 0], X_train[np.where(y == 1), 1], marker="o", c="b")
plt.scatter(X_train[np.where(y == -1), 0], X_train[np.where(y == -1), 1], marker="x", c="r")
return line,
def update(i):
global history_w, history_b, ax, line
w = history_w[i]
b = history_b[i]
if w[1] == 0:
return line,
x1 = -1
y1 = -(b + w[0] * x1) / w[1]
x2 = 6
y2 = -(b + w[0] * x2) / w[1]
line.set_data([x1, x2], [y1, y2])
return line,
plt.xlim(-1, 6)
plt.ylim(-1, 4)
print(history_w)
print(history_b)
#[[[3, 3], 1], [[2, 2], 0], [[1, 1], -1], [[0, 0], -2], [[3, 3], -1], [[2, 2], -2], [[1, 1], -3]]
ani = anim.FuncAnimation(fig=fig, func=update,init_func=init, frames=len(history_b), interval=1000, repeat=True, blit=True)
plt.show()
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