灰狼算法分类预测代码

灰狼算法是一种新型的优化算法，主要用于求解非线性的优化问题。与其他优化算法相比，灰狼算法具有收敛速度快、精度高、适应性强等优点。下面是利用灰狼算法进行分类预测的代码示例：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target

# 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 灰狼算法分类器
class GWOClassifier:
    def __init__(self, num_wolves=3, max_iter=30):
        self.num_wolves = num_wolves
        self.max_iter = max_iter
        
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
    
    def initialize(self, num_features):
        self.alpha_pos = np.zeros(num_features + 1)
        self.alpha_score = float("inf")
        self.beta_pos = np.zeros(num_features + 1)
        self.beta_score = float("inf")
        self.delta_pos = np.zeros(num_features + 1)
        self.delta_score = float("inf")
        
        self.positions = np.random.rand(self.num_wolves, num_features + 1)
        self.scores = np.zeros(self.num_wolves)
        
    def predict(self, X):
        X = np.hstack((np.ones((X.shape[0], 1)), X))
        y_pred = np.round(self.sigmoid(np.dot(X, self.alpha_pos)))
        return y_pred.astype(int)
    
    def fit(self, X, y):
        num_samples, num_features = X.shape
        
        self.initialize(num_features)
        
        for iteration in range(self.max_iter):
            a = 2 - (2 * (iteration / self.max_iter))
            
            for i in range(self.num_wolves):
                for j in range(num_features + 1):
                    r1 = np.random.rand()
                    r2 = np.random.rand()
                    A1 = 2 * a * r1 - a
                    C1 = 2 * r2
                    D_alpha = abs(C1 * self.alpha_pos[j] - self.positions[i][j])
                    X1 = self.alpha_pos[j] - A1 * D_alpha
                    
                    r1 = np.random.rand()
                    r2 = np.random.rand()
                    A2 = 2 * a * r1 - a
                    C2 = 2 * r2
                    D_beta = abs(C2 * self.beta_pos[j] - self.positions[i][j])
                    X2 = self.beta_pos[j] - A2 * D_beta
                    
                    r1 = np.random.rand()
                    r2 = np.random.rand()
                    A3 = 2 * a * r1 - a
                    C3 = 2 * r2
                    D_delta = abs(C3 * self.delta_pos[j] - self.positions[i][j])
                    X3 = self.delta_pos[j] - A3 * D_delta
                    
                    self.positions[i][j] = (X1 + X2 + X3) / 3
            
            for i in range(self.num_wolves):
                self.scores[i] = np.mean(self.predict(X) != y)
                
                if self.scores[i] < self.alpha_score:
                    self.alpha_score = self.scores[i]
                    self.alpha_pos = self.positions[i]
                elif self.scores[i] < self.beta_score:
                    self.beta_score = self.scores[i]
                    self.beta_pos = self.positions[i]
                elif self.scores[i] < self.delta_score:
                    self.delta_score = self.scores[i]
                    self.delta_pos = self.positions[i]
                    
        return self

使用方法如下：

clf = GWOClassifier(num_wolves=5, max_iter=50)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

其中，`num_wolves`和`max_iter`是算法中的两个关键参数，需要根据实际情况进行调整。