pso-svm多分类python代码

以下是使用PSO-SVM进行多分类的Python代码示例：

import numpy as np
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 生成多分类数据集
X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, n_classes=3, random_state=42)

# 定义PSO-SVM分类器
class PSOSVM:
    def __init__(self, n_particles=10, max_iter=100, c1=2, c2=2, w=0.7):
        self.n_particles = n_particles
        self.max_iter = max_iter
        self.c1 = c1
        self.c2 = c2
        self.w = w

    def fit(self, X, y):
        # 数据标准化
        scaler = StandardScaler()
        X_scaled = scaler.fit_transform(X)

        # 初始化粒子群
        particles = np.random.uniform(low=-1, high=1, size=(self.n_particles, X.shape\[1\] + 1))
        velocities = np.zeros_like(particles)
        best_positions = particles.copy()
        best_scores = np.zeros(self.n_particles)

        # 迭代更新粒子群
        for _ in range(self.max_iter):
            for i in range(self.n_particles):
                # 计算粒子的适应度得分
                svm = SVC(C=10 ** particles\[i\]\[-1\])
                svm.fit(X_scaled, y)
                y_pred = svm.predict(X_scaled)
                score = accuracy_score(y, y_pred)

                # 更新粒子的最佳位置和最佳得分
                if score > best_scores\[i\]:
                    best_positions\[i\] = particles\[i\].copy()
                    best_scores\[i\] = score

            # 更新粒子的速度和位置
            for i in range(self.n_particles):
                r1 = np.random.rand(X.shape\[1\] + 1)
                r2 = np.random.rand(X.shape\[1\] + 1)
                velocities\[i\] = self.w * velocities\[i\] + self.c1 * r1 * (best_positions\[i\] - particles\[i\]) + self.c2 * r2 * (best_positions.mean(axis=0) - particles\[i\])
                particles\[i\] += velocities\[i\]

        # 找到最佳粒子的位置
        best_particle = particles\[np.argmax(best_scores)\]

        # 根据最佳粒子的位置训练最终的SVM分类器
        svm = SVC(C=10 ** best_particle\[-1\])
        svm.fit(X_scaled, y)

        self.svm = svm
        self.scaler = scaler

    def predict(self, X):
        X_scaled = self.scaler.transform(X)
        return self.svm.predict(X_scaled)

# 使用PSO-SVM进行多分类
pso_svm = PSOSVM(n_particles=10, max_iter=100, c1=2, c2=2, w=0.7)
pso_svm.fit(X, y)
y_pred = pso_svm.predict(X)

# 计算准确率
accuracy = accuracy_score(y, y_pred)
print("Accuracy:", accuracy)

请注意，这只是一个示例代码，具体的PSO-SVM实现可能会有所不同。你可以根据自己的需求进行调整和修改。
#### 引用[.reference_title]
- *1* *2* *3* [基于Python的SVM算法深入研究](https://blog.csdn.net/weixin_45137708/article/details/106340493)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^koosearch_v1,239^v3^insert_chatgpt"}} ] [.reference_item]
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