期望最大化算法

用于对企业人事进行管理，包含各种信息的管理，学习winform的案例(Used in the management of enterprise personnel, including the management of various information, learn the case of WinForm)

semantic web overview

Uma modelagem de um cenario basico para quem esta aprend o a usar open/gl na plataforma C++. Utilizando comandos basicos como setas direcionais e teclas especiais como home, end, page up e page down voce pode navegar por todo o ambiente 3D com muita facilidade.-Uma modelagem de um cenario basico para quem esta aprendendo a usar open/gl na plataforma C++. Utilizando comandos basicos como setas direcionais e teclas especiais como home, end, page up e page down voce pode navegar por todo o ambiente 3D com muita facilidade.

说明：  应用Matlab软件程序，遗传算法问题解决车辆路径问题(Using Matlab software to solve the problem of cars routing problems with genetic or evolution algorithm in mathematics.)

说明：  计算燃气轮机燃气热物性，混合燃气的焓值、比热等参数(Calculating the thermal properties of gas turbine, enthalpy and specific heat of mixed gas)

凡四大皆空暖风机看四大皆空妇女三角裤赴河南江苏 。-Where nothing heaters women look nothing briefs to Henan and Jiangsu.

说明：  Over 80 object-oriented recipes to help you create mindblowing GUIs in Python

LS-esprit算法求解波达方位估计DOA(LS_ESPRIT_Algorithm_for_DOA_Estimation_with_Monte_Carlo)

9电平MMC逆变器，开环控制，明显可以看出9电平逆变，采用载波移相调制(9-level MMC Inverter, Open-loop)

说明：  1) 使用凝聚型层次聚类算法（即最小生成树算法）对所有数据点进行聚类，最后聚成3类。相异度定义方法可选择single linkage、complete linkage、average linkage或者average group linkage中任意一种。 2) 使用C-Means算法对所有数据点进行聚类。C=3。 任务2（必做）： 使用高斯混合模型（GMM）聚类算法对所有数据点进行聚类。C=3。并请给出得到的混合模型参数（包括比例??、均值??和协方差Σ）。 任务3（全做）： 1) 参考数据文件第三列的类标签，使用聚类有效性评价的外部方法Normalized Mutual Information指标，分别计算任务1和任务2聚类结果的有效性。 2) 使用聚类有效性评价的内部方法Xie-Beni指标，分别计算任务1和任务2聚类结果的有效性。(The main results are as follows: 1) the condensed hierarchical clustering algorithm (that is, the minimum spanning tree algorithm) is used to cluster all the data points, and finally it is grouped into three categories. Any of the single linkage,complete linkage,average linkage or average group linkage methods can be selected for the definition of dissimilarity. 2) using C-Means algorithm to cluster all data points. C = 3.)