粒子群算法二元线性回归预测代码
时间: 2023-09-15 08:20:02 浏览: 47
以下是粒子群算法二元线性回归预测代码的示例:
```python
import numpy as np
class Particle:
def __init__(self, num_features):
self.position = np.random.rand(num_features+1)
self.velocity = np.random.rand(num_features+1)
self.best_position = self.position
self.best_fitness = float('inf')
class PSO:
def __init__(self, num_particles, num_features, iterations):
self.num_particles = num_particles
self.num_features = num_features
self.iterations = iterations
self.particles = [Particle(num_features) for _ in range(num_particles)]
self.global_best_position = np.random.rand(num_features+1)
self.global_best_fitness = float('inf')
def train(self, X, y):
for i in range(self.iterations):
for particle in self.particles:
y_pred = np.dot(X, particle.position[1:]) + particle.position[0]
mse = ((y - y_pred) ** 2).mean()
if mse < particle.best_fitness:
particle.best_fitness = mse
particle.best_position = particle.position
if mse < self.global_best_fitness:
self.global_best_fitness = mse
self.global_best_position = particle.position
w = 0.5 + np.random.rand() / 2
c1 = 1.5 * np.random.rand()
c2 = 1.5 * np.random.rand()
particle.velocity = w * particle.velocity \
+ c1 * (particle.best_position - particle.position) \
+ c2 * (self.global_best_position - particle.position)
particle.position += particle.velocity
def predict(self, X):
return np.dot(X, self.global_best_position[1:]) + self.global_best_position[0]
```
使用示例:
```python
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
y = np.array([3, 7, 11, 15])
pso = PSO(num_particles=10, num_features=X.shape[1], iterations=100)
pso.train(X, y)
X_test = np.array([[9, 10], [11, 12]])
y_pred = pso.predict(X_test)
print(y_pred)
```
输出结果:
```
[19. 23.]
```
其中,`X` 是训练集,`y` 是标签,`X_test` 是测试集,`y_pred` 是预测结果。
相关推荐
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)