给出不使用sklearn库实现svm实现手写数字识别的python代码

以下是一个简单的使用Python实现SVM手写数字识别的示例代码，不使用sklearn库：

import numpy as np

class SVM:
    def __init__(self, learning_rate=0.001, lambda_param=0.01, n_iters=1000):
        self.lr = learning_rate
        self.lambda_param = lambda_param
        self.n_iters = n_iters
        self.w = None
        self.b = None
        
    def fit(self, X, y):
        n_samples, n_features = X.shape
        
        y_ = np.where(y <= 0, -1, 1)
        
        self.w = np.zeros(n_features)
        self.b = 0
        
        for _ in range(self.n_iters):
            for idx, x_i in enumerate(X):
                condition = y_[idx] * (np.dot(x_i, self.w) - self.b) >= 1
                if condition:
                    self.w -= self.lr * (2 * self.lambda_param * self.w)
                else:
                    self.w -= self.lr * (2 * self.lambda_param * self.w - np.dot(x_i, y_[idx]))
                    self.b -= self.lr * y_[idx]
        
    def predict(self, X):
        approx = np.dot(X, self.w) - self.b
        return np.sign(approx)

def main():
    digits = datasets.load_digits()
    X, y = digits.data, digits.target
    svm = SVM()
    svm.fit(X, y)
    print(svm.predict(X))

if __name__ == '__main__':
    main()

这个示例需要手动从`sklearn.datasets`中导入`load_digits`来获取手写数字数据集。注意，这个示例不是最优的实现方式，它只是为了展示如何使用SVM实现手写数字识别。