关于虹膜数据不收敛的Python感知器代码

import numpy as np

class Perceptron:
    def __init__(self, learning_rate=0.1, max_iterations=100):
        self.learning_rate = learning_rate
        self.max_iterations = max_iterations

    def train(self, X, y):
        self.weights = np.zeros(X.shape[1])
        self.bias = 0

        for _ in range(self.max_iterations):
            for i in range(X.shape[0]):
                prediction = self.predict(X[i])
                error = y[i] - prediction
                self.weights += self.learning_rate * error * X[i]
                self.bias += self.learning_rate * error

    def predict(self, X):
        activation = np.dot(X, self.weights) + self.bias
        return 1 if activation >= 0 else 0

# 虹膜数据集示例
X = np.array([[5.1, 3.5, 1.4, 0.2],
              [4.9, 3.0, 1.4, 0.2],
              [5.8, 2.6, 4.0, 1.2],
              [6.7, 3.0, 5.2, 2.3]])
y = np.array([0, 0, 1, 1])

# 创建感知器对象并进行训练
perceptron = Perceptron()
perceptron.train(X, y)

# 进行预测
test_data = np.array([5.0, 3.3, 1.4, 0.2])
prediction = perceptron.predict(test_data)
print("预测结果:", prediction)