设计一套pso-kmeans代码,测试不同K值对分类结果的影响。
时间: 2024-05-15 21:16:22 浏览: 115
以下是一套基于Python的PSO-Kmeans代码,用于测试不同K值对分类结果的影响。
```python
import numpy as np
from sklearn.cluster import KMeans
class Particle:
def __init__(self, dim, k, lb, ub):
self.k = k
self.dim = dim
self.lb = lb
self.ub = ub
self.position = np.random.uniform(lb, ub, (k, dim))
self.velocity = np.random.uniform(lb, ub, (k, dim))
self.fitness = float('inf')
self.pbest_position = self.position.copy()
self.pbest_fitness = float('inf')
def update_fitness(self, data):
kmeans = KMeans(n_clusters=self.k, init=self.position, n_init=1).fit(data)
self.fitness = kmeans.inertia_
if self.fitness < self.pbest_fitness:
self.pbest_fitness = self.fitness
self.pbest_position = self.position.copy()
def update_position_velocity(self, gbest_position, w, c1, c2):
r1 = np.random.rand(self.k, self.dim)
r2 = np.random.rand(self.k, self.dim)
self.velocity = w*self.velocity + c1*r1*(self.pbest_position - self.position) + c2*r2*(gbest_position - self.position)
self.position = np.clip(self.position + self.velocity, self.lb, self.ub)
class PSO_Kmeans:
def __init__(self, n_particles, n_iterations, dim, k, lb, ub, w, c1, c2):
self.n_particles = n_particles
self.n_iterations = n_iterations
self.dim = dim
self.k = k
self.lb = lb
self.ub = ub
self.w = w
self.c1 = c1
self.c2 = c2
self.particles = [Particle(dim, k, lb, ub) for _ in range(n_particles)]
self.gbest_fitness = float('inf')
self.gbest_position = np.random.uniform(lb, ub, (k, dim))
def run(self, data):
for i in range(self.n_iterations):
for particle in self.particles:
particle.update_fitness(data)
if particle.fitness < self.gbest_fitness:
self.gbest_fitness = particle.fitness
self.gbest_position = particle.position.copy()
for particle in self.particles:
particle.update_position_velocity(self.gbest_position, self.w, self.c1, self.c2)
kmeans = KMeans(n_clusters=self.k, init=self.gbest_position, n_init=1).fit(data)
return kmeans.labels_
if __name__ == '__main__':
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
data, labels = make_blobs(n_samples=1000, centers=3, random_state=42)
pso_kmeans = PSO_Kmeans(n_particles=10, n_iterations=100, dim=data.shape[1], k=3, lb=-10, ub=10, w=0.7, c1=1.5, c2=1.5)
predicted_labels = pso_kmeans.run(data)
fig, axs = plt.subplots(1, 2, figsize=(10, 5))
axs[0].scatter(data[:, 0], data[:, 1], c=labels)
axs[0].set_title('True Labels')
axs[1].scatter(data[:, 0], data[:, 1], c=predicted_labels)
axs[1].set_title('Predicted Labels')
plt.show()
```
在这个示例中,我们使用`make_blobs`函数生成一个带有3个中心的数据集,并使用PSO-Kmeans算法进行聚类。我们测试不同的K值对聚类结果的影响,如下所示:
```python
if __name__ == '__main__':
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
data, labels = make_blobs(n_samples=1000, centers=3, random_state=42)
k_values = [2, 3, 4, 5, 6]
for k in k_values:
pso_kmeans = PSO_Kmeans(n_particles=10, n_iterations=100, dim=data.shape[1], k=k, lb=-10, ub=10, w=0.7, c1=1.5, c2=1.5)
predicted_labels = pso_kmeans.run(data)
fig, axs = plt.subplots(1, 2, figsize=(10, 5))
axs[0].scatter(data[:, 0], data[:, 1], c=labels)
axs[0].set_title('True Labels')
axs[1].scatter(data[:, 0], data[:, 1], c=predicted_labels)
axs[1].set_title('Predicted Labels (K = {})'.format(k))
plt.show()
```
在这个示例中,我们使用了K值为2,3,4,5,6进行测试,每个K值都生成一个包含原始数据和聚类结果的图表。这些图表可以用来比较不同K值对聚类结果的影响。
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