PSO-SVM代码实现
时间: 2023-09-30 13:07:52 浏览: 105
以下是基于PSO算法的SVM代码实现(Python版本):
```python
import numpy as np
class PSO_SVM:
def __init__(self, c1=1, c2=1, w=0.6, max_iter=100, num_particles=10, kernel='linear'):
self.c1 = c1
self.c2 = c2
self.w = w
self.max_iter = max_iter
self.num_particles = num_particles
self.kernel = kernel
self.X = None
self.y = None
self.n_samples = None
self.n_features = None
self.alpha = None
self.b = None
self.particles_alpha = None
self.particles_b = None
self.particles_v_alpha = None
self.particles_v_b = None
self.particles_loss = None
self.gbest_alpha = None
self.gbest_b = None
self.gbest_loss = float('inf')
def fit(self, X, y):
self.X = X
self.y = y
self.n_samples, self.n_features = X.shape
self.alpha = np.zeros(self.n_samples)
self.b = 0
self.particles_alpha = np.zeros((self.num_particles, self.n_samples))
self.particles_b = np.zeros(self.num_particles)
self.particles_v_alpha = np.zeros((self.num_particles, self.n_samples))
self.particles_v_b = np.zeros(self.num_particles)
self.particles_loss = np.zeros(self.num_particles)
for i in range(self.num_particles):
self.particles_alpha[i] = np.random.uniform(low=0, high=1, size=self.n_samples)
self.particles_b[i] = np.random.uniform(low=0, high=1)
self.particles_v_alpha[i] = np.zeros(self.n_samples)
self.particles_v_b[i] = 0
self.particles_loss[i] = self.loss(self.particles_alpha[i], self.particles_b[i])
if self.particles_loss[i] < self.gbest_loss:
self.gbest_alpha = self.particles_alpha[i]
self.gbest_b = self.particles_b[i]
self.gbest_loss = self.particles_loss[i]
for _ in range(self.max_iter):
for i in range(self.num_particles):
r1 = np.random.uniform(low=0, high=1, size=self.n_samples)
r2 = np.random.uniform(low=0, high=1)
self.particles_v_alpha[i] = self.w * self.particles_v_alpha[i] \
+ self.c1 * r1 * (self.particles_alpha[i] - self.gbest_alpha) \
+ self.c2 * r2 * (self.particles_alpha[i] - self.alpha)
self.particles_v_b[i] = self.w * self.particles_v_b[i] \
+ self.c1 * r1 * (self.particles_b[i] - self.gbest_b) \
+ self.c2 * r2 * (self.particles_b[i] - self.b)
self.particles_alpha[i] = np.clip(self.particles_alpha[i] + self.particles_v_alpha[i], 0, 1)
self.particles_b[i] += self.particles_v_b[i]
self.particles_loss[i] = self.loss(self.particles_alpha[i], self.particles_b[i])
if self.particles_loss[i] < self.gbest_loss:
self.gbest_alpha = self.particles_alpha[i]
self.gbest_b = self.particles_b[i]
self.gbest_loss = self.particles_loss[i]
self.alpha = self.gbest_alpha
self.b = self.gbest_b
def predict(self, X_test):
y_pred = np.zeros(len(X_test))
if self.kernel == 'linear':
for i in range(len(X_test)):
y_pred[i] = np.sign(np.dot(self.alpha * self.y, np.dot(self.X, X_test[i])) + self.b)
return y_pred
def loss(self, alpha, b):
if self.kernel == 'linear':
y_pred = np.dot(self.alpha * self.y, np.dot(self.X, self.X.T)) + b
loss = np.sum(alpha) - 0.5 * np.sum((alpha * self.y)[:, None] * (self.y * y_pred))
return loss
```
其中,PSO算法的核心部分在`fit`方法中,其中`r1`和`r2`分别代表随机因子,`particles_v_alpha`和`particles_v_b`分别代表粒子的速度,`particles_alpha`和`particles_b`分别代表粒子的位置,`self.gbest_alpha`和`self.gbest_b`分别代表全局最优位置,`self.particles_loss`代表粒子的损失函数值。其中,损失函数的实现采用的是线性核函数。