运用LIFSTDP-SOM模型进行分类的PYTHON代码

以下是一个使用 LIFSTDP-SOM 模型进行分类的简单 Python 代码示例：

import numpy as np

class LIFSTDP:
    def __init__(self, input_dim, map_dim, learning_rate, tau_pre, tau_post):
        self.input_dim = input_dim
        self.map_dim = map_dim
        self.learning_rate = learning_rate
        self.tau_pre = tau_pre
        self.tau_post = tau_post
        self.weights = np.random.rand(map_dim[0], map_dim[1], input_dim)
        self.pre_trace = np.zeros((map_dim[0], map_dim[1]))
        self.post_trace = np.zeros((map_dim[0], map_dim[1]))

    def lif(self, inputs):
        self.weights -= self.learning_rate * (self.weights - inputs)

    def stdp(self, pre_spike, post_spike):
        self.pre_trace = self.pre_trace * np.exp(-pre_spike / self.tau_pre) + pre_spike
        self.post_trace = self.post_trace * np.exp(-post_spike / self.tau_post) + post_spike
        delta_weights = np.outer(self.pre_trace, self.post_trace)
        self.weights += self.learning_rate * delta_weights

    def som(self, input_data, labels, num_iterations):
        for _ in range(num_iterations):
            for input_vector, label in zip(input_data, labels):
                distances = np.sum((self.weights - input_vector) ** 2, axis=2)
                winner = np.unravel_index(np.argmin(distances), distances.shape)
                self.lif(input_vector)
                self.stdp(pre_spike=np.eye(self.map_dim[0], self.map_dim[1])[winner],
                          post_spike=np.eye(self.map_dim[0], self.map_dim[1]))
                self.learn(label, winner)

    def learn(self, label, winner):
        if label == 1 and self.weights[winner] < 0.5:
            self.weights[winner] += 0.1
        elif label == 0 and self.weights[winner] > 0.5:
            self.weights[winner] -= 0.1

    def classify(self, input_data):
        distances = np.sum((self.weights - input_data) ** 2, axis=2)
        winner = np.unravel_index(np.argmin(distances), distances.shape)
        return winner

# 示例使用
input_data = np.array([[0.2, 0.4, 0.6], [0.1, 0.3, 0.5], [0.7, 0.8, 0.9]])
labels = [1, 0, 1]
lif_stdp_som = LIFSTDP(input_dim=3, map_dim=(5, 5), learning_rate=0.1, tau_pre=20, tau_post=20)
lif_stdp_som.som(input_data, labels, num_iterations=100)

# 测试分类
test_input = np.array([0.2, 0.3, 0.4])
winner = lif_stdp_som.classify(test_input)
print("Test Input:", test_input)
print("Classified as winner:", winner)

在这个示例代码中，我们对 LIFSTDP-SOM 模型进行了一些修改以进行分类任务。在 `som()` 方法中，我们添加了一个 `labels` 参数来指定每个输入向量的标签。在每次训练迭代中，我们调用 `learn()` 方法来根据标签对获胜神经元的权重进行调整。

在示例使用部分，我们创建了一个 `LIFSTDP` 对象，并使用输入数据和标签进行训练。然后，我们使用 `classify()` 方法对一个测试输入进行分类，并打印获胜神经元的位置。

请注意，这只是一个简单的示例代码，实际应用中可能需要根据具体需求进行修改和扩展。