python实现svm支持向量机算法代码

import numpy as np class LinearSVM: def __init__(self, learning_rate=0.01, n_iters=1000, lambda_param=0.01): self.lr = learning_rate self.n_iters = n_iters self.lambda_param = lambda_param self.w = None self.b = None def fit(self, X, y): n_samples, n_features = X.shape y_ = np.where(y <= 0, -1, 1) # 将标签转换为-1和1 # 初始化参数 self.w = np.zeros(n_features) self.b = 0 # 梯度下降 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: continue 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): linear_output = np.dot(X, self.w) - self.b return np.sign(linear_output) # 示例数据 X = np.array([[2, 3], [5, 8], [6, 1], [7, 2], [1, 1], [2, 1], [3, 2], [1, 0], [2, 0], [4, 1]]) y = np.array([0, 1, 1, 1, 0, 0, 0, -1, -1, -1]) # 实例化SVM模型 model = LinearSVM(learning_rate=0.01, n_iters=1000, lambda_param=0.01) # 训练模型 model.fit(X, y) # 预测 predictions = model.predict(X) print(predictions)