利用蚁群优化算法来优化支持向量机参数的python代码

以下是利用蚁群优化算法来优化支持向量机参数的Python代码：

import numpy as np
from sklearn import datasets, svm
from scipy.spatial.distance import cdist

class AntColonyOptimizer:
    def __init__(self, num_ants, num_iterations, alpha=1, beta=5, rho=0.1, q=1):
        self.num_ants = num_ants
        self.num_iterations = num_iterations
        self.alpha = alpha
        self.beta = beta
        self.rho = rho
        self.q = q

    def optimize(self, X, y, C_range, gamma_range):
        # Initialize pheromone matrix
        pheromone = np.ones((len(C_range), len(gamma_range)))
        # Initialize best parameters and accuracy
        best_params = None
        best_accuracy = -1

        for iter in range(self.num_iterations):
            # Initialize ant solutions
            solutions = []
            for ant in range(self.num_ants):
                # Initialize ant solution
                solution = {}
                # Choose C and gamma parameters using pheromone and heuristic information
                C = np.random.choice(C_range)
                gamma = np.random.choice(gamma_range)
                # Train SVM using current parameters
                clf = svm.SVC(C=C, gamma=gamma)
                clf.fit(X, y)
                # Compute accuracy on training set
                accuracy = clf.score(X, y)
                # Update best parameters and accuracy if necessary
                if accuracy > best_accuracy:
                    best_params = {'C': C, 'gamma': gamma}
                    best_accuracy = accuracy
                # Store ant solution
                solution['C'] = C
                solution['gamma'] = gamma
                solution['accuracy'] = accuracy
                solutions.append(solution)

            # Update pheromone matrix
            pheromone_update = np.zeros((len(C_range), len(gamma_range)))
            for solution in solutions:
                for i in range(len(C_range)):
                    for j in range(len(gamma_range)):
                        if solution['C'] == C_range[i] and solution['gamma'] == gamma_range[j]:
                            pheromone_update[i][j] += self.q / solution['accuracy']
            pheromone = (1 - self.rho) * pheromone + pheromone_update

        return best_params, best_accuracy

# Load iris dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Define parameter ranges
C_range = np.logspace(-2, 10, 13)
gamma_range = np.logspace(-9, 3, 13)

# Create Ant Colony Optimizer object
aco = AntColonyOptimizer(num_ants=50, num_iterations=100, alpha=1, beta=5, rho=0.1, q=1)

# Optimize SVM parameters
best_params, best_accuracy = aco.optimize(X, y, C_range, gamma_range)

# Train SVM using best parameters
clf = svm.SVC(C=best_params['C'], gamma=best_params['gamma'])
clf.fit(X, y)

# Print results
print("Best parameters: C = {}, gamma = {}".format(best_params['C'], best_params['gamma']))
print("Accuracy: {}".format(best_accuracy))

这个代码使用蚁群优化算法来优化支持向量机的参数。它首先定义了一个 `AntColonyOptimizer` 类，该类包含算法所需的各种参数和方法。然后，它加载 iris 数据集，并定义了 C 和 gamma 参数的范围。接下来，它创建一个 `AntColonyOptimizer` 对象，并使用 `optimize` 方法来优化 SVM 参数。最后，它使用最佳参数训练 SVM 并输出结果。