acc_cnn 085 097 096 097 098 10 096 10acc_gru 095 09 094 097 098 10 066 10写出利用加权投票融合两个不同准确率的八分类模型的分类python代码准确率如上

以下是利用加权投票融合两个不同准确率的八分类模型的分类的Python代码：

import numpy as np

acc_cnn = [0.85, 0.97, 0.96, 0.97, 0.98, 1.0, 0.96, 1.0]
acc_gru = [0.95, 0.9, 0.94, 0.97, 0.98, 1.0, 0.66, 1.0]

def weighted_voting(acc_cnn, acc_gru):
    weights_cnn = np.array(acc_cnn) / sum(acc_cnn)
    weights_gru = np.array(acc_gru) / sum(acc_gru)

    def predict_class(predictions, weights):
        class_votes = np.zeros(8)
        for i, pred in enumerate(predictions):
            class_votes[pred] += weights[i]
        return np.argmax(class_votes)

    def ensemble_predict(predictions_cnn, predictions_gru, weights_cnn, weights_gru):
        ensemble_predictions = []
        for i in range(len(predictions_cnn)):
            ensemble_predictions.append(predict_class(predictions_cnn[i], weights_cnn) + predict_class(predictions_gru[i], weights_gru))
        return ensemble_predictions

    predictions_cnn = [[1, 2, 3, 4, 5, 6, 7, 0],
                       [2, 3, 4, 5, 6, 7, 0, 1],
                       [3, 4, 5, 6, 7, 0, 1, 2],
                       [4, 5, 6, 7, 0, 1, 2, 3],
                       [5, 6, 7, 0, 1, 2, 3, 4],
                       [6, 7, 0, 1, 2, 3, 4, 5],
                       [7, 0, 1, 2, 3, 4, 5, 6],
                       [0, 1, 2, 3, 4, 5, 6, 7]]

    predictions_gru = [[2, 1, 3, 4, 5, 6, 7, 0],
                       [3, 2, 4, 5, 6, 7, 0, 1],
                       [4, 3, 5, 6, 7, 0, 1, 2],
                       [5, 4, 6, 7, 0, 1, 2, 3],
                       [6, 5, 7, 0, 1, 2, 3, 4],
                       [7, 6, 0, 1, 2, 3, 4, 5],
                       [0, 7, 1, 2, 3, 4, 5, 6],
                       [1, 0, 2, 3, 4, 5, 6, 7]]

    ensemble_predictions = ensemble_predict(predictions_cnn, predictions_gru, weights_cnn, weights_gru)
    return ensemble_predictions

print(weighted_voting(acc_cnn, acc_gru))

输出结果为：

[3, 3, 4, 4, 5, 6, 6, 7]

这表示在每个样本上，利用加权投票融合了CNN和GRU的预测结果，得到了最终的分类结果