-
Notifications
You must be signed in to change notification settings - Fork 34
/
Copy pathperceptron.py
91 lines (70 loc) · 2.7 KB
/
perceptron.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @File : perceptron.py
# @Data : 2020/5/20
# @Author : Luo Kun
# @Contact: luokun485@gmail.com
import numpy as np
from matplotlib import pyplot as plt
class Perceptron:
"""
Perceptron classifier(感知机分类器)
"""
def __init__(self, input_dim: int, lr=5e-4):
"""
Args:
input_dim (int): 输入特征维度
"""
self.weights = np.random.randn(input_dim + 1) # 权重
self.lr = lr # 学习率
def fit(self, X: np.ndarray, y: np.ndarray):
for x, y in zip(pad(X), y):
if y * (x @ self.weights) <= 0: # 分类错误, y 与 wx + b 符号不同
neg_grad = x * y # 计算weights的负梯度
self.weights += self.lr * neg_grad # 沿负梯度方向更新weights
def __call__(self, X: np.ndarray) -> np.ndarray:
y_pred = pad(X) @ self.weights
return np.where(y_pred > 0, 1, -1)
def pad(x):
return np.concatenate([x, np.ones([len(x), 1])], axis=1)
def load_data(n_samples_per_class=500):
X = np.concatenate([
np.random.randn(n_samples_per_class, 2) + np.array([1, -1]),
np.random.randn(n_samples_per_class, 2) + np.array([-1, 1]),
])
y = np.array([-1] * n_samples_per_class + [1] * n_samples_per_class)
training_set, test_set = np.split(np.random.permutation(len(X)), [int(len(X) * 0.6)])
return X, y, training_set, test_set
def train_perceptron(model, X, y, epochs=100):
indices = np.arange(len(X))
for _ in range(epochs):
np.random.shuffle(indices)
model.fit(X[indices], y[indices])
if __name__ == "__main__":
X, y, training_set, test_set = load_data()
plt.figure(figsize=[12, 6])
plt.subplot(1, 2, 1)
plt.title("Ground Truth")
plt.xlim(-4, 4)
plt.ylim(-4, 4)
plt.scatter(X[y == -1, 0], X[y == -1, 1], marker=".")
plt.scatter(X[y == +1, 0], X[y == +1, 1], marker=".")
perceptron = Perceptron(input_dim=2)
train_perceptron(perceptron, X[training_set], y[training_set])
y_pred = perceptron(X)
acc = np.sum(y_pred[test_set] == y[test_set]) / len(test_set)
print(f"Accuracy = {100 * acc:.2f}%")
plt.subplot(1, 2, 2)
plt.title("Prediction")
plt.xlim(-4, 4)
plt.ylim(-4, 4)
plt.scatter(X[y_pred == -1, 0], X[y_pred == -1, 1], marker=".")
plt.scatter(X[y_pred == +1, 0], X[y_pred == +1, 1], marker=".")
w = perceptron.weights
a, b = -w[0] / w[1], -w[2] / w[1]
line_x = np.linspace(-4, 4, 400)
line_y = a * line_x + b
plt.plot(line_x, line_y, lw=1)
plt.fill_between(line_x, np.full(400, -4), line_y, alpha=0.1)
plt.fill_between(line_x, np.full(400, +4), line_y, alpha=0.1)
plt.show()