These_code_snippets_utilize_the_Gaussian_Mixture_M_IoT-Device-

# GMM-Gaussian-Mixture-Model- These code snippets utilize the Gaussian Mixture Model (GMM) to forecast future data for solar energy and channel gain in IoT devices. Within the GMM.py file, there are three distinct GMM fitting methods tailored for variables of different dimensions. 这些代码片段使用高斯混合模型(GMM)来预测太阳能和IoT设备中的信道增益的未来数据。在GMM.py文件中，有三种针对不同维度变量的GMM拟合方法。 mec3 is developed using Pyomo and represents a MILP (Mixed Integer Linear Programming) model. RHC serves as an experimental file to analyze the energy consumption of heuristic solutions. test is an experimental file designed to evaluate the energy consumption of both optimal and random solutions. random_decision is tailored for generating random strategies concerning channel allocation and mode execution approximations. Both NetworkBasics and Solar are crucial files responsible for generating key parameters. Notably, both them were provided by my tutor. mec3 是使用Pyomo开发的，并代表一个MILP（混合整数线性编程）模型。 RHC 作为一个实验文件，用来分析启发式解决方案的能耗。 test 是一个实验文件，旨在评估最优解和随机解的能耗。 random_decision 专门用于生成关于信道分配和近似模式执行的随机策略。 NetworkBasics 和 Solar 都是生成关键参数的关键文件, 值得注意的是，它们两个都是由我的导师提供的的。