聚类人工蜂群算法python

以下是基于Python的聚类人工蜂群算法的代码示例：

import numpy as np
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

class ABC:
    def __init__(self, n_clusters, n_employed_bees, n_onlooker_bees, n_iterations):
        self.n_clusters = n_clusters
        self.n_employed_bees = n_employed_bees
        self.n_onlooker_bees = n_onlooker_bees
        self.n_iterations = n_iterations
        self.dimension = None
        self.lower_bound = None
        self.upper_bound = None
        self.food_sources = None
        self.trial_counts = None
        self.fitness_values = None
        self.best_source = None
        self.best_fitness = None

    def fit(self, X):
        self.dimension = X.shape[1]
        self.lower_bound = np.min(X, axis=0)
        self.upper_bound = np.max(X, axis=0)
        self.food_sources = np.random.uniform(self.lower_bound, self.upper_bound, size=(self.n_employed_bees, self.dimension))
        self.trial_counts = np.zeros(self.n_employed_bees)
        self.fitness_values = np.zeros(self.n_employed_bees)
        self.evaluate(X)
        for i in range(self.n_iterations):
            self.send_employed_bees()
            self.send_onlooker_bees(X)
            self.select_best_source()
            self.send_scout_bees(X)

    def evaluate(self, X):
        kmeans = KMeans(n_clusters=self.n_clusters, init=self.food_sources, n_init=1).fit(X)
        self.fitness_values = kmeans.inertia_

    def send_employed_bees(self):
        for i in range(self.n_employed_bees):
            j = np.random.choice([x for x in range(self.n_employed_bees) if x != i])
            phi = np.random.uniform(-1, 1, size=self.dimension)
            new_food_source = self.food_sources[i] + phi * (self.food_sources[i] - self.food_sources[j])
            new_fitness_value = self.evaluate_new_source(new_food_source)
            if new_fitness_value < self.fitness_values[i]:
                self.food_sources[i] = new_food_source
                self.fitness_values[i] = new_fitness_value
                self.trial_counts[i] = 0
            else:
                self.trial_counts[i] += 1

    def evaluate_new_source(self, source):
        source = np.clip(source, self.lower_bound, self.upper_bound)
        kmeans = KMeans(n_clusters=self.n_clusters, init=source.reshape(1, -1), n_init=1).fit(X)
        return kmeans.inertia_

    def send_onlooker_bees(self, X):
        probabilities = self.fitness_values / np.sum(self.fitness_values)
        for i in range(self.n_onlooker_bees):
            j = np.random.choice(range(self.n_employed_bees), p=probabilities)
            phi = np.random.uniform(-1, 1, size=self.dimension)
            new_food_source = self.food_sources[j] + phi * (self.food_sources[j] - self.food_sources[np.random.choice([x for x in range(self.n_employed_bees) if x != j])])
            new_fitness_value = self.evaluate_new_source(new_food_source)
            if new_fitness_value < self.fitness_values[j]:
                self.food_sources[j] = new_food_source
                self.fitness_values[j] = new_fitness_value
                self.trial_counts[j] = 0
            else:
                self.trial_counts[j] += 1

    def select_best_source(self):
        index = np.argmin(self.fitness_values)
        if self.best_fitness is None or self.fitness_values[index] < self.best_fitness:
            self.best_fitness = self.fitness_values[index]
            self.best_source = self.food_sources[index]

    def send_scout_bees(self, X):
        for i in range(self.n_employed_bees):
            if self.trial_counts[i] >= self.n_employed_bees:
                self.food_sources[i] = np.random.uniform(self.lower_bound, self.upper_bound, size=self.dimension)
                self.trial_counts[i] = 0
                self.evaluate(X)

    def predict(self, X):
        kmeans = KMeans(n_clusters=self.n_clusters, init=self.best_source.reshape(1, -1), n_init=1).fit(X)
        return kmeans.labels_

if __name__ == '__main__':
    X, y = make_blobs(n_samples=1000, centers=3, n_features=2, random_state=42)
    abc = ABC(n_clusters=3, n_employed_bees=10, n_onlooker_bees=10, n_iterations=100)
    abc.fit(X)
    print(abc.predict(X))

该示例使用sklearn的make_blobs函数生成了一个随机数据集，然后使用聚类人工蜂群算法对其进行聚类。算法的核心循环中包含了四个步骤：(1) 送出雇用蜂，(2) 送出观察蜂，(3) 选择最佳食物源，(4) 送出侦查蜂。在每个步骤中，都会更新食物源的位置和适应度值，并记录试验次数。算法最终输出数据点的聚类标签。