diagonal sum

This code is an exercise that adds the diagonal elements of any matrix. The sum of the diagonal elements can be easily calculated using MATLAB functions. To achieve this, use the built-in function to access and sum up the elements along the diagonal of the matrix.

MySQL中的合计函数SUM用于计算指定列的总和，语法为：SELECT SUM(列名) FROM 表名 [WHERE 条件]。例如，统计一个班级数学总成绩可以使用SUM(math)，同时也可以对多列使用SUM进行计算，但需要确保列值为数值类型。另外，SUM函数还可以与WHERE子句结合使用，以便筛选出特定条件下的总和结果。

在SQL语法中，SUM函数用于计算指定列的数值总和。例如，可以使用以下语句计算员工工资总和：SELECT SUM(SALARY) FROM EMPLOYEE_PAY_TBL。如果要计算不重复的工资总和，可以使用SELECT SUM(DISTINCT SALARY) FROM EMPLOYEE_PAY_TBL。

SQL聚合函数包括MAX（最大值）、MIN（最小值）、AVG（平均值）、SUM（总和）、COUNT（计数），处理数据时不计算null值。在处理男学生出生日期的最大值和最小值时，使用SELECT MAX(sBirthday), MIN(sBirthday) FROM student WHERE sSex='男'。另外，类型转换可以使用CAST(expression AS data_type[length])或CONVERT(data_type[length], expression)，用于数据类型转换。例如，计算平均成绩使用SELECT AVG(english) FROM score。COUNT(*)返回所有项数，包括NULL和重复项。对于唯一非空值的计算可以使用COUNT(DISTINCT expression)。例如，使用AdventureWorks2008R2数据库查询不同职位数量的例子如下：USE AdventureWorks2008R2; SELECT COUNT(DISTINCT JobTitle) FROM HumanResources.Employee;

SUM函数仅适用于数字类型数据。语法如下：SUM([DISTINCT] 列名)，示例：SELECT SUM(SALARY) FROM EMPLOYEE_PAY_TBL计算工资总和。SELECT SUM(DISTINCT SALARY) FROM EMPLOYEE_PAY_TBL计算去重后的工资总和。

SUM和AVG函数用于在MySQL中分别计算表达式中的所有值项的总和与平均值。其语法格式如下： SUM / AVG ( [ ALL | DISTINCT ] expression ) 示例：计算学生总成绩 【例7.34】假设我们需要计算学号为081101的学生所修课程的总成绩，可以通过以下SQL语句实现： SELECT SUM(成绩) AS '课程总成绩' FROM CJB WHERE 学号 = '081101'; 查询结果将显示学号081101的学生所学课程的总成绩。 注意：SUM() 函数计算数值的累加，AVG() 函数则用于计算平均值，支持 ALL 和 DISTINCT 修饰符以控制是否包含重复值。

图模型在机器学习领域应用广泛。与传统图模型相比，Sum-Product Networks (SPN) 模型具有更强的表达能力和更快的推理速度，因此在文本和图像数据建模方面得到广泛应用。 SPN 是一种新型深度概率模型。固定结构 SPN 的参数学习方法为模型训练提供了基础。研究人员也针对不同输入数据，探索了 SPN 结构和参数的联合学习方法，进一步提升了模型的灵活性。 SPN 支持判别式和生成式模型，为不同类型的机器学习任务提供了有力工具。实践证明，SPN 在文本分类任务中表现出色，展现了其在处理复杂数据方面的潜力。

SQL聚合函数包括SUM、AVG、MAX和MIN。SUM用于计算总和，AVG计算平均值，MAX和MIN分别用于获取最大和最小值。示例：SELECT SUM(Upoint) as 总积分 FROM Customers; SELECT AVG(Upoint) as 平均积分 FROM Customers; SELECT MAX(Upoint) as 最高积分, MIN(Upoint) as 最低积分 FROM Customers;

Energy Control Problem Code in MATLAB: Non-Intrusive Load Monitoring (NILM) for HVAC Systems This repository contains the dataset we collected for HVAC energy disaggregation, as well as the source code and demonstrations from our paper in IEEE Transactions on Power Systems. To the best of our knowledge, this is the first dataset collected for studying Non-Intrusive Load Monitoring (NILM) applied to Heating, Ventilation, and Air Conditioning (HVAC) systems. Energy disaggregation or Non-Intrusive Load Monitoring (NILM) addresses the problem of extracting device-level energy consumption information by monitoring the aggregated signal at a single measurement point, without the need to install meters on each individual device. This can be framed as a source separation problem where the aggregated signal is represented as a linear combination of the basic vectors in a matrix factorization framework. In this work, we utilize machine learning to predict the energy consumption pattern of each device over the course of a day. The project is part of our collaboration with [institution name]. Prerequisites: MATLAB R2015a Datasets (Temporarily unavailable. Will be available once the required permissions are granted. Apologies for the inconvenience!) Experiments We designed two different experiments to evaluate our proposed algorithm. The first experiment disaggregates the energy of the entire household into the energy consumption of all devices within the home.