请你在不使用from sklearn import svm的条件下写一个性能优越的svm函数

在Python中，如果你不直接引用sklearn库中的SVM类，而是手动实现SVM的核心逻辑，将会涉及到一些复杂的工作，因为sklearn已经对SVM算法进行了优化。不过，我们可以通过scipy库中的优化模块`optimize`以及numpy来实现基本的支持向量机模型。这是一个简化的二分类线性SVM的实现：

import numpy as np
from scipy.optimize import minimize
from scipy.sparse import spdiags

def _sgd_loss(X, y, w, C):
    b = w[0]
    w = w[1:]
    margins = np.dot(X, w) + b - y * np.linalg.norm(w)
    misclass = np.where(y * margins < 1, margins, 0)
    l1_penalty = np.abs(w).sum() / len(w)
    l2_penalty = C * np.square(w).sum() / len(w)
    return np.mean(misclass) + l1_penalty + l2_penalty

def _gradient(X, y, w, C):
    b = w[0]
    w = w[1:]
    grad_w = np.zeros_like(w)
    grad_b = 0
    for i in range(len(X)):
        margin = np.dot(X[i], w) + b - y[i] * np.linalg.norm(w)
        if margin < 1 and y[i] > 0:
            grad_b -= 1
            grad_w -= X[i]
        elif margin < 1 and y[i] < 0:
            grad_b += 1
            grad_w += X[i]
        else:
            continue
    grad_w /= len(X)
    grad_b /= len(X)
    l1_part = np.sign(w) / len(w)
    l2_part = C * w / len(w)
    return np.concatenate([grad_b, grad_w + l1_part - l2_part])

def lbfgs_svm(X, y, max_iter=100, C=1.0, tol=1e-4):
    n_samples, n_features = X.shape
    w = np.zeros(n_features + 1)
    bounds = [(None, None)] * (n_features + 1)

    options = {'maxiter': max_iter, 'ftol': tol}
    res = minimize(_sgd_loss, w, args=(X, y, C), method='L-BFGS-B', jac=_gradient, bounds=bounds, options=options)
    
    return res.x[1:], res.x[0]

# 使用示例
X_train, y_train = ... # 你的训练数据
w, bias = lbfgs_svm(X_train, y_train)