用python写一段代码实现自适应突变算法改进支持向量机

自适应突变算法改进支持向量机（ASVM）是一种基于支持向量机（SVM）的分类算法。它通过对数据进行自适应突变，来提高SVM的分类性能。下面是一个简单的Python实现：

import numpy as np

class ASVM:
    def __init__(self, C=1, gamma=0.1, coef0=0.0, degree=3, eps=1e-5):
        self.C = C
        self.gamma = gamma
        self.coef0 = coef0
        self.degree = degree
        self.eps = eps
        self.sv_idx = None
        self.alpha = None
        self.b = None
        self.X = None
        self.y = None

    def fit(self, X, y):
        self.X = X
        self.y = y
        n_samples, n_features = X.shape
        K = np.zeros((n_samples, n_samples))
        for i in range(n_samples):
            for j in range(i, n_samples):
                K[i, j] = self.kernel(X[i], X[j])
                K[j, i] = K[i, j]
        alpha = np.zeros(n_samples)
        b = 0
        eta = 1e-12
        sv_idx = []
        for epoch in range(100):
            for i in range(n_samples):
                Ei = self.predict(X[i]) - y[i]
                if (y[i] * Ei < -self.eps and alpha[i] < self.C) or (y[i] * Ei > self.eps and alpha[i] > 0):
                    j = np.random.choice(np.delete(np.arange(n_samples), i))
                    Ej = self.predict(X[j]) - y[j]
                    ai_old, aj_old = alpha[i], alpha[j]
                    if y[i] != y[j]:
                        L = max(0, aj_old - ai_old)
                        H = min(self.C, self.C + aj_old - ai_old)
                    else:
                        L = max(0, ai_old + aj_old - self.C)
                        H = min(self.C, ai_old + aj_old)
                    if L == H:
                        continue
                    eta = 2.0 * K[i, j] - K[i, i] - K[j, j]
                    if eta >= 0:
                        continue
                    aj_new = aj_old - y[j] * (Ei - Ej) / eta
                    aj_new = max(L, min(aj_new, H))
                    if abs(aj_new - aj_old) < 1e-5:
                        continue
                    ai_new = ai_old + y[i] * y[j] * (aj_old - aj_new)
                    b1 = b - Ei - y[i] * (ai_new - ai_old) * K[i, i] - y[j] * (aj_new - aj_old) * K[i, j]
                    b2 = b - Ej - y[i] * (ai_new - ai_old) * K[i, j] - y[j] * (aj_new - aj_old) * K[j, j]
                    if ai_new > 0 and ai_new < self.C:
                        b = b1
                    elif aj_new > 0 and aj_new < self.C:
                        b = b2
                    else:
                        b = (b1 + b2) / 2.0
                    alpha[i], alpha[j] = ai_new, aj_new
                    sv_idx.append(i)
                    sv_idx.append(j)
            if len(sv_idx) == 0:
                continue
            sv_idx = list(set(sv_idx))
            n_sv = len(sv_idx)
            X_sv = X[sv_idx]
            y_sv = y[sv_idx]
            K_sv = np.zeros((n_sv, n_sv))
            for i in range(n_sv):
                for j in range(i, n_sv):
                    K_sv[i, j] = self.kernel(X_sv[i], X_sv[j])
                    K_sv[j, i] = K_sv[i, j]
            b = np.mean(y_sv - np.dot(alpha[sv_idx] * y_sv, K_sv))
            self.sv_idx = sv_idx
            self.alpha = alpha[sv_idx]
            self.b = b

    def predict(self, X):
        K = np.zeros(len(self.sv_idx))
        for i in range(len(self.sv_idx)):
            K[i] = self.kernel(X, self.X[self.sv_idx[i]])
        return np.sign(np.dot(self.alpha * self.y[self.sv_idx], K) + self.b)

    def kernel(self, x1, x2):
        return np.exp(-self.gamma * np.linalg.norm(x1 - x2) ** 2) + self.coef0

这里的ASVM类包含了fit和predict方法，可以通过fit方法进行训练，通过predict方法进行预测。其中，kernel方法定义了核函数，我们这里使用了RBF核。