分类：计算机代写

计算机代写|python代考|COMP90059

Posted on 2023年8月21日2023年8月28日 by statistics-lab

如果你也在怎样代写python这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。Python是一种解释性的、面向对象的、具有动态语义的高级编程语言，由Guido van Rossum开发。它最初于1991年发布。“Python”这个名字的设计既简单又有趣，是对英国喜剧团体Monty Python的致敬。Python以对初学者友好而闻名，取代Java成为使用最广泛的入门语言，因为它为用户处理了许多复杂性，允许初学者专注于完全掌握编程概念，而不是微小的细节。

Python用于服务器端web开发、软件开发、数学和系统脚本，并且由于其高级内置数据结构、动态类型和动态绑定，它在快速应用程序开发中很受欢迎，并且作为脚本或粘合语言来绑定现有组件。由于易于学习的语法和对可读性的强调，Python降低了程序维护成本。此外，Python对模块和包的支持促进了模块化程序和代码的重用。Python是一种开源社区语言，因此许多独立的程序员不断地为它构建库和功能。

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计算机代写|python代考|Beginning to Use Python — Strings

At this point, you should feel free to experiment with using the shell’s basic behavior. Type some text, in quotes; for starters, you could type the following:

“This text really won’t do anything”
“This text really won’t do anything”
$>>>$
$\gg>$ “This text really won’t do anything”
“This text really won’t do anything”

You should notice one thing immediately: After you entered a quote (“), codeEditor’s Python shell changed the color of everything up to the quote that completed the sentence. Of course, the preceding text is absolutely true. It did nothing: It didn’t change your Python environment; it was merely evaluated by the running Python instance, in case it did determine that in fact you’d told it to do something. In this case, you’ve asked it only to read the text you wrote, but doing this doesn’t constitute a change to the environment.
However, you can see that Python indicated that it saw what you entered. It showed you the text you entered, and it displayed it in the manner it will always display a string – in quotes. As you learn about other data types, you’ll find that Python has a way of displaying each one differently.
What Is a String?
The string is the first data type that you’re being introduced to within Python. Computers in general, and programming languages specifically, segregate everything they deal with into types. Types are categories for things within a program with which the program will work. After a thing has a type, the program (and the programmer) knows what to do with that thing. This is a fundamental aspect of how computers work, because without a named type for the abstract ideas that they work with, the computer won’t know how to do basic things like combine two different values. However, if you have two things, and they’re of the same type, you can define easy rules for combining them. Therefore, when the type of a thing has been confirmed, Python knows what its options are, and you as the programmer know more about what to do with it.

计算机代写|python代考|Why the Quotes?

Now, back to strings in particular. Strings are the basic unit of text in Python. Unlike some other programming languages, a single letter is represented as a one-letter string. Instead of trying to explain strings in terms of other concepts in a vacuum, let’s create some examples of strings using the Python shell and build from there.
Try It Out Entering Strings with Different Quotes
Enter the following strings, keeping in mind the type of quotes (single or double) and the ends of lines (use the Enter key when you see that the end of a line has been reached):
$>>$ “This is another string”
‘This is another string’
$>>$ ‘This is also a string’
‘This is also a string’
$>>$ ” “This is a third string that is some
$\ldots$ how different” “
‘This is a third string that is some $\backslash$ n how different’
$\gg>$ “This is another string”
‘This is another string’
$>>$ ‘This is also a string’
‘This is also a string’
$>>$ ” “This is a third string that is some
… how different” ” “
‘This is a third string that is some $\backslash n$ how different’
How It Works
If you use different quotes, they may look different to you; to the Python interpreter; however all of them can be used in the same situations and are very similar. For more information, read on.

These examples raise a few questions. In your first text example, you saw that the text was enclosed in double quotes, and when python saw two quotes it repeated those double quotes on the next line. However, in the preceding example, double quotes are used for “This is another string”, but below it single quotes are used. Then, in the third example, three double quotes in a row are used, and after the word “some” we used the Enter key, which caused a new line to appear. The following section explains these seemingly arbitrary conventions.

python代写

计算机代写|python代考|Beginning to Use Python — Strings

此时，您应该可以自由地尝试使用shell的基本行为。在引号中键入一些文本;对于初学者，您可以输入以下内容:

“这篇文章真的没什么用”
“这篇文章真的没什么用”
美元$ > > >
$\gg>$ “这段文字真的没什么用”
“这篇文章真的没什么用”

您应该立即注意到一件事:在您输入引号(‘ ‘)之后，codeEditor的Python shell更改了所有内容的颜色，直到完成句子的引号。当然，前面的文字是绝对正确的。它什么也没做:它没有改变你的Python环境;它只是由正在运行的Python实例计算，以防它确定实际上您已经告诉它做某事。在这种情况下，您只要求它读取您编写的文本，但这样做并不构成对环境的改变。
但是，您可以看到Python指示它看到了您输入的内容。它显示了您输入的文本，并以总是显示字符串的方式显示它-在引号中。当您了解其他数据类型时，您会发现Python有一种显示每种数据类型的方式。
什么是字符串?
字符串是Python中引入的第一个数据类型。一般来说，计算机，特别是编程语言，会把它们处理的所有东西都划分为类型。类型是程序中与程序一起工作的事物的类别。当一个东西有了类型之后，程序(和程序员)就知道该怎么处理这个东西了。这是计算机工作的一个基本方面，因为如果没有为它们处理的抽象概念命名类型，计算机就不知道如何做一些基本的事情，比如组合两个不同的值。但是，如果您有两个相同类型的东西，则可以定义简单的规则来组合它们。因此，当一个东西的类型被确认后，Python就知道它的选项是什么，而作为程序员的你就知道该如何处理它。

计算机代写|python代考|Why the Quotes?

现在，回到字符串。字符串是Python中文本的基本单位。与其他编程语言不同，单个字母表示为单字母字符串。与其在真空中尝试用其他概念来解释字符串，不如让我们使用Python shell创建一些字符串示例，并从那里开始构建。
尝试用不同的引号输入字符串
输入以下字符串，记住引号的类型(单引号或双引号)和行尾(当您看到行尾时使用Enter键):
$>>$ “这是另一个字符串”
“这是另一个字符串”
$>>$ ‘这也是一个字符串’
“这也是一个字符串”
$>>$ ” “这是第三个字符串
$\ldots$有何不同” “
‘这是第三个字符串，$\反斜杠$ n有什么不同’
$\gg>$ “这是另一个字符串”
“这是另一个字符串”
$>>$ ‘这也是一个字符串’
“这也是一个字符串”
$>>$ ” “这是第三个字符串
……多么不同啊!”
‘这是第三个字符串，$\反斜杠n$有什么不同’
它是如何工作的
如果你使用不同的引号，它们对你来说可能看起来不一样;Python解释器;然而，它们都可以在相同的情况下使用，并且非常相似。欲了解更多信息，请继续阅读。

这些例子提出了一些问题。在您的第一个文本示例中，您看到文本用双引号括起来，当python看到两个引号时，它在下一行重复这些双引号。然而，在前面的例子中，双引号用于“这是另一个字符串”，但在它下面使用单引号。然后，在第三个示例中，一行中使用了三个双引号，在单词“some”之后，我们使用Enter键，这导致出现新的一行。下一节将解释这些看似随意的约定。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题，以及设计新的和创新的金融产品。

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

术语广义线性模型（GLM）通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归，以及方差分析和方差分析（仅含固定效应）。

有限元方法代写

有限元方法（FEM）是一种流行的方法，用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。

有限元是一种通用的数值方法，用于解决两个或三个空间变量的偏微分方程（即一些边界值问题）。为了解决一个问题，有限元将一个大系统细分为更小、更简单的部分，称为有限元。这是通过在空间维度上的特定空间离散化来实现的，它是通过构建对象的网格来实现的：用于求解的数值域，它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统，以模拟整个问题。然后，有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

随机分析代写

随机微积分是数学的一个分支，对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。

时间序列分析代写

随机过程，是依赖于参数的一组随机变量的全体，参数通常是时间。随机变量是随机现象的数量表现，其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值（如1秒，5分钟，12小时，7天，1年），因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中，往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录，以得到其自身发展的规律。

回归分析代写

多元回归分析渐进（Multiple Regression Analysis Asymptotics）属于计量经济学领域，主要是一种数学上的统计分析方法，可以分析复杂情况下各影响因素的数学关系，在自然科学、社会和经济学等多个领域内应用广泛。

MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习和应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|Java代写|CSC2040

Posted on 2023年8月21日2023年8月28日 by statistics-lab

如果你也在怎样代写JAVA 这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。JAVA是一种广泛使用的面向对象编程语言和软件平台，在数十亿台设备上运行，包括笔记本电脑、移动设备、游戏机、医疗设备和许多其他设备。Java的规则和语法是基于C和c++语言的。

JAVA开发软件的一个主要优点是它的可移植性。一旦在笔记本电脑上为Java程序编写了代码，就很容易将代码转移到移动设备上。当Sun Microsystems(后来被Oracle收购)的James Gosling在1991年发明这种语言时，主要目标是能够“编写一次，随处运行”。

statistics-lab™ 为您的留学生涯保驾护航在代写Java方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写Java代写方面经验极为丰富，各种代写Java相关的作业也就用不着说。

计算机代写|Java代写|Programming Errors

Errors that are detected by the compiler are called syntax errors or compile errors. Syntax errors result from errors in code construction, such as mistyping a keyword, omitting some necessary punctuation, or using an opening brace without a corresponding closing brace. These errors are usually easy to detect, because the compiler tells you where they are and what caused them. For example, the program in Listing 1.5 has a syntax error, as shown in Figure 1.19.
Listing I.5 ShowSyntaxErrors.java
\begin{tabular}{ll}
1 & public class ShowSyntaxErrors { \
2 & public static main(String[] args) { \
3 & System.out.println(“Welcome to Java); \
4 & \multirow{2}{*}{}} \
5 & }
\end{tabular}
public class ShowSyntaxErrors {
public static main(String[] args) {
System.out.println (“Welcome to Java);
}
}
Four errors are reported, but the program actually has two errors:

The keyword void is missing before main in line 2 .

The string Welcome to Java should be closed with a closing quotation mark in line 3 .
Since a single error will often display many lines of compile errors, it is a good practice to fix errors from the top line and work downward. Fixing errors that occur earlier in the program may also fix additional errors that occur later.

Runtime errors are errors that cause a program to terminate abnormally. They occur while a program is running if the environment detects an operation that is impossible to carry out. Input mistakes typically cause runtime errors. An input error occurs when the program is waiting for the user to enter a value, but the user enters a value that the program cannot handle. For instance, if the program expects to read in a number, but instead the user enters a string, this causes data-type errors to occur in the program.

Another example of runtime errors is division by zero. This happens when the divisor is zero for integer divisions. For instance, the program in Listing 1.6 would cause a runtime error, as shown in Figure 1.20.
LISTING I.6 ShowRuntimeErrors.java
$\begin{array}{ll}1 & \text { public class ShowRuntimeErrors }{ \ 2 & \text { public static void main(String }[] \text { args) }{ \ 3 & \text { System.out.println(1/0); } \ 4 & 3\end{array}$
public class ShowRuntimeErrors {
public static void main(String[] args) {
System.out.println $(1 / 0)$;
}
}

计算机代写|Java代写|Logic Errors

Logic errors occur when a program does not perform the way it was intended to. Errors of this logic errors kind occur for many different reasons. For example, suppose you wrote the program in Listing 1.7 to convert Celsius 35 degrees to a Fahrenheit degree:
LISTING I.7 ShowLogicErrors.java
$\begin{array}{ll}1 & \text { public class ShowLogicErrors { } \ 2 & \text { public static void main(String[] args) { } \ 3 & \text { System.out.println(“Celsius } 35 \text { is Fahrenheit degree “); } \ 4 & \text { System.out.println( }(9 / 5) * 35+32) ; \ 5 & }\end{array}$
public class ShowLogicErrors {
public static void main(String[] args) {
System.out.println(“Celsius 35 is Fahrenheit degree “);
System.out.println $((9 / 5) * 35+32)$;
}
}

You will get Fahrenheit 67 degrees, which is wrong. It should be 95.0. In Java, the division for integers is an integer-the fractional part is truncated-so in Java 9 / 5 is 1 . To get the correct result, you need to use $9.0 / 5$, which results in 1.8 .

In general, syntax errors are easy to find and easy to correct, because the compiler gives indications as to where the errors came from and why they are wrong. Runtime errors are not difficult to find, either, since the reasons and locations for the errors are displayed on the console when the program aborts. Finding logic errors, on the other hand, can be very challenging. In the upcoming chapters, you will learn the techniques of tracing programs and finding logic errors.

Java代考

计算机代写|Java代写|Programming Errors

编译器检测到的错误称为语法错误或编译错误。语法错误是由代码结构中的错误引起的，例如输入错误的关键字，遗漏了一些必要的标点符号，或者使用了开始大括号而没有相应的结束大括号。这些错误通常很容易检测到，因为编译器会告诉您它们在哪里以及是什么原因导致的。例如，清单1.5中的程序有一个语法错误，如图1.19所示。
清单1 .5 ShowSyntaxErrors.java
\begin{tabular}{ll}
1 & public class ShowSyntaxErrors { \2 & public static main(String[] args) { \3 & System.out.println(“Welcome to Java);\4 &\multirow{2}{*}{}} \5 &}
\end{tabular}
公共类ShowSyntaxErrors
{public static main(String[] args){
System.out.println(“欢迎来到Java); 报告了}

四个错误，但程序实际上有两个错误:}

在第2行main之前缺少关键字void。

字符串Welcome to Java应该在第3行中以结束引号结束。
由于单个错误通常会显示多行编译错误，因此从最上面一行开始修复错误并向下工作是一种很好的做法。修复程序中较早发生的错误也可能修复较晚发生的其他错误。

运行时错误是导致程序异常终止的错误。当程序运行时，如果环境检测到一个不可能执行的操作，就会发生这种情况。输入错误通常会导致运行时错误。当程序等待用户输入一个值，但是用户输入了一个程序无法处理的值时，就会发生输入错误。例如，如果程序期望读入一个数字，但用户却输入了一个字符串，这将导致程序中出现数据类型错误。

运行时错误的另一个例子是除零。这种情况发生在除数为零的整数除法中。例如，清单1.6中的程序会导致运行时错误，如图1.20所示。
清单1 .6 ShowRuntimeErrors.java
$\begin{array}{ll}1 & \text { public class ShowRuntimeErrors }{ \ 2 & \text { public static void main(String }[] \text { args) }{ \ 3 & \text { System.out.println(1/0); } \ 4 & 3\end{array}$
公共类ShowRuntimeErrors
{public static void main(String[] args){
System.out.println $(1 / 0)$;
}
}

计算机代写|Java代写|Logic Errors

当程序没有按照预期的方式执行时，就会发生逻辑错误。这种逻辑错误有很多不同的原因。例如，假设您编写了清单1.7中的程序来将摄氏35度转换为华氏度:
清单1 .7 ShowLogicErrors.java
$\begin{array}{ll}1 & \text { public class ShowLogicErrors { } \ 2 & \text { public static void main(String[] args) { } \ 3 & \text { System.out.println(“Celsius } 35 \text { is Fahrenheit degree “); } \ 4 & \text { System.out.println( }(9 / 5) * 35+32) ; \ 5 & }\end{array}$
公共类ShowLogicErrors
{public static void main(String[] args){
系统，退出。println(“摄氏35度是华氏度”);
System.out.println $((9 / 5) * 35+32)$;
}
}

你会得到华氏67度，这是错误的。应该是95度。在Java中，整数的除法是整数——小数部分被截断——所以在Java中9 / 5是1。要获得正确的结果，需要使用$9.0 / 5$，结果是1.8。

一般来说，语法错误很容易发现，也很容易纠正，因为编译器会提示错误的来源和错误的原因。运行时错误也不难发现，因为当程序终止时，错误的原因和位置会显示在控制台上。另一方面，发现逻辑错误可能非常具有挑战性。在接下来的章节中，您将学习跟踪程序和查找逻辑错误的技术。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|Java代写|CSC225

Posted on 2023年8月21日2023年8月28日 by statistics-lab

计算机代写|Java代写|The Java Language Specification, API, JDK, and IDE

Computer languages have strict rules of usage. If you do not follow the rules when writing a program, the computer will not be able to understand it. The Java language specification and the Java API define the Java standards.

The Java language specification is a technical definition of the Java programming language’s syntax and semantics. You can find the complete Java language specification at java.sun.com/docs/books/jls.

The application program interface (API), also known as library, contains predefined classes and interfaces for developing Java programs. The API is still expanding. You can view and download the latest version of the Java API at www.oracle.com/technetwork/java/index.html.
Java is a full-fledged and powerful language that can be used in many ways. It comes in three editions:

Java Standard Edition (Java SE) to develop client-side standalone applications or applets.

Java Enterprise Edition (Java EE) to develop server-side applications, such as Java servlets, JavaServer Pages (JSP), and JavaServer Faces (JSF).

Java Micro Edition (Java ME) to develop applications for mobile devices, such as cell phones.

This book uses Java SE to introduce Java programming. Java SE is the foundation upon which all other Java technology is based. There are many versions of Java SE. The latest, Java SE 7, is used in this book. Oracle releases each version with a Java Development Toolkit $(J D K)$. For Java SE 7, the Java Development Toolkit is called JDK 1.7 (also known as Java 7 or $J D K 7)$,

The JDK consists of a set of separate programs, each invoked from a command line, for developing and testing Java programs. Instead of using the JDK, you can use a Java development tool (e.g., NetBeans, Eclipse, and TextPad)—software that provides an integrated development environment (IDE) for developing Java programs quickly. Editing, compiling, building, debugging, and online help are integrated in one graphical user interface. You simply enter source code in one window or open an existing file in a window, and then click a button or menu item or press a function key to compile and run the program.

计算机代写|Java代写|Creating, Compiling, and Executing a Java Program

You have to create your program and compile it before it can be executed. This process is repetitive, as shown in Figure 1.14. If your program has compile errors, you have to modify the program to fix them, then recompile it. If your program has runtime errors or does not produce the correct result, you have to modify the program, recompile it, and execute it again.

You can use any text editor or IDE to create and edit a Java source-code file. This section demonstrates how to create, compile, and run Java programs from a command window. If you wish to use an IDE such as Eclipse, NetBeans, or TextPad, refer to Supplement II for tutorials. From the command window, you can use a text editor such as Notepad to create the Java source-code file, as shown in Figure 1.15.

A Java compiler translates a Java source file into a Java bytecode file. The following command compiles Welcome.java:
javac Welcome.java

If there aren’t any syntax errors, the compiler generates a bytecode file with a .class extension. Thus, the preceding command generates a file named Welcome.class, as shown in Figure 1.16a. The Java language is a high-level language, but Java bytecode is a low-level language. The bytecode is similar to machine instructions but is architecture neutral and can run on any platform that has a Java Virtual Machine (JVM), as shown in Figure 1.16b. Rather than a physical machine, the virtual machine is a program that interprets Java bytecode. This is one of Java’s primary advantages: Java bytecode can run on a variety of hardware platforms and operating systems. Java source code is compiled into Java bytecode and Java bytecode is interpreted by the JVM. Your Java code may use the code in the Java library. The JVM executes your code along with the code in the library.

To execute a Java program is to run the program’s bytecode. You can execute the bytecode on any platform with a JVM, which is an interpreter. It translates the individual instructions in the bytecode into the target machine language code one at a time rather than the whole program as a single unit. Each step is executed immediately after it is translated.

Java代考

机代写|Java代写|The Java Language Specification, API, JDK, and IDE

计算机语言有严格的使用规则。如果你在编写程序时不遵循规则，计算机将无法理解它。Java语言规范和Java API定义了Java标准。

Java语言规范是Java编程语言的语法和语义的技术定义。您可以在java.sun.com/docs/books/jls上找到完整的Java语言规范。

应用程序编程接口(API)，也称为库，包含用于开发Java程序的预定义类和接口。API仍在扩展。您可以在www.oracle.com/technetwork/java/index.html上查看和下载最新版本的Java API。
Java是一种成熟而强大的语言，可以以多种方式使用。它有三个版本:

Java标准版(Java SE)，用于开发客户端独立应用程序或小程序。

Java Enterprise Edition (Java EE)用于开发服务器端应用程序，例如Java servlet、JavaServer Pages (JSP)和JavaServer Faces (JSF)。

Java Micro Edition (Java ME)用于开发移动设备(如手机)的应用程序。

本书使用Java SE来介绍Java编程。Java SE是所有其他Java技术所基于的基础。Java SE有很多版本。本书中使用了最新的Java SE 7。Oracle发布每个版本时都附带一个Java开发工具包$(J D K)$。对于Java SE 7, Java开发工具包称为JDK 1.7(也称为Java 7或$ jdk7)$，

JDK由一组独立的程序组成，每个程序都从命令行调用，用于开发和测试Java程序。代替使用JDK，您可以使用Java开发工具(例如，NetBeans、Eclipse和TextPad)——这些软件为快速开发Java程序提供了集成开发环境(IDE)。编辑、编译、构建、调试和在线帮助集成在一个图形用户界面中。您只需在一个窗口中输入源代码或在窗口中打开现有文件，然后单击按钮或菜单项或按下功能键即可编译并运行程序。

计算机代写|Java代写|Creating, Compiling, and Executing a Java Program

你必须创建你的程序，并在它可以执行之前编译它。这个过程是重复的，如图1.14所示。如果您的程序有编译错误，您必须修改程序来修复它们，然后重新编译它。如果您的程序有运行时错误或不能产生正确的结果，您必须修改程序，重新编译，然后再次执行它。

您可以使用任何文本编辑器或IDE来创建和编辑Java源代码文件。本节演示如何从命令窗口创建、编译和运行Java程序。如果您希望使用Eclipse、NetBeans或TextPad等IDE，请参考附录II获取教程。在命令窗口中，您可以使用文本编辑器(如Notepad)来创建Java源代码文件，如图1.15所示。

Java编译器将Java源文件转换为Java字节码文件。下面的命令编译Welcome.java:
javac Welcome.java

如果没有任何语法错误，编译器将生成一个扩展名为.class的字节码文件。因此，上述命令生成一个名为Welcome.class的文件，如图1.16a所示。Java语言是一种高级语言，而Java字节码是一种低级语言。字节码类似于机器指令，但与体系结构无关，可以在任何具有Java虚拟机(JVM)的平台上运行，如图1.16b所示。虚拟机不是物理机，而是解释Java字节码的程序。这是Java的主要优点之一:Java字节码可以在各种硬件平台和操作系统上运行。Java源代码被编译成Java字节码，Java字节码由JVM进行解释。您的Java代码可以使用Java库中的代码。JVM与库中的代码一起执行您的代码。

执行Java程序就是运行程序的字节码。您可以在任何有JVM的平台上执行字节码，JVM是一个解释器。它将字节码中的单个指令一次一个地翻译成目标机器语言代码，而不是将整个程序作为单个单元。每个步骤在翻译后立即执行。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|Java代写|COMP9103

Posted on 2023年8月21日2023年8月28日 by statistics-lab

计算机代写|Java代写|Programming Languages

Computers do not understand human languages, so programs must be written in a language a computer can use. There are hundreds of programming languages, and they were developed to make the programming process easier for people. However, all programs must be converted into a language the computer can understand.

A computer’s native language, which differs among different types of computers, is its machine language – a set of built-in primitive instructions. These instructions are in the form machine language of binary code, so if you want to give a computer an instruction in its native language, you have to enter the instruction as binary code. For example, to add two numbers, you might have to write an instruction in binary code, like this:
$$
1101101010011010
$$

Programming in machine language is a tedious process. Moreover, programs written in machine language are very difficult to read and modify. For this reason, assembly language was created in the early days of computing as an alternative to machine languages. Assembly language uses a short descriptive word, known as a mnemonic, to represent each of the machine-language instructions. For example, the mnemonic add typically means to add numbers and sub means to subtract numbers. To add the numbers 2 and 3 and get the result, you might write an instruction in assembly code like this:
add 2,3 , result
Assembly languages were developed to make programming easier. However, because the computer cannot understand assembly language, another program — called an assembler – is used to translate assembly-language programs into machine code, as shown in Figure 1.8.

Writing code in assembly language is easier than in machine language. However, it is still tedious to write code in assembly language. An instruction in assembly language essentially corresponds to an instruction in machine code. Writing in assembly requires that you know how the CPU works. Assembly language is referred to as a low-level language, because assembly language is close in nature to machine language and is machine dependent.

计算机代写|Java代写|High-Level Language

In the 1950 s, a new generation of programming languages known as high-level languages emerged. They are platform-independent, which means that you can write a program in a highlevel language and run it in different types of machines. High-level languages are English-like and easy to learn and use. The instructions in a high-level programming language are called statements. Here, for example, is a high-level language statement that computes the area of a circle with a radius of 5 :
$$
\text { area }=5 * 5 * 3.1415
$$
There are many high-level programming languages, and each was designed for a specific purpose. Table 1.1 lists some popular ones.

A program written in a high-level language is called a source program or source code. Because a computer cannot understand a source program, a source program must be translated into machine code for execution. The translation can be done using another programming tool called an interpreter or a compiler.

An interpreter reads one statement from the source code, translates it to the machine code or virtual machine code, and then executes it right away, as shown in Figure 1.9a.

Java代考

计算机代写|Java代写|Programming Languages

计算机不懂人类语言，所以程序必须用计算机能使用的语言编写。有数百种编程语言，它们的发展使编程过程对人们来说更容易。然而，所有的程序都必须转换成计算机能理解的语言。

计算机的母语(在不同类型的计算机中有所不同)是它的机器语言——一组内置的原始指令。这些指令是以二进制代码的机器语言形式出现的，所以如果你想用计算机的母语给它一条指令，你必须以二进制代码的形式输入指令。例如，要将两个数字相加，你可能需要用二进制代码编写一条指令，如下所示:
$$
1101101010011010
$$

用机器语言编程是一个冗长乏味的过程。此外，用机器语言编写的程序很难阅读和修改。由于这个原因，汇编语言在计算的早期被创造出来，作为机器语言的替代品。汇编语言使用一个简短的描述词，称为助记符，来表示每条机器语言指令。例如，助记符add通常表示数字的加法，而sub表示数字的减法。要将数字2和3相加并得到结果，您可以在汇编代码中编写这样的指令:
加2,3，结果
开发汇编语言是为了使编程更容易。然而，由于计算机不能理解汇编语言，另一个程序——称为汇编程序——被用来将汇编语言程序翻译成机器码，如图1.8所示。

用汇编语言写代码比用机器语言写代码容易。然而，用汇编语言编写代码仍然很乏味。汇编语言中的指令本质上相当于机器码中的指令。用汇编编写程序要求您知道CPU是如何工作的。汇编语言被称为低级语言，因为汇编语言在本质上接近机器语言，并且依赖于机器。

计算机代写|Java代写|High-Level Language

在20世纪50年代，被称为高级语言的新一代编程语言出现了。它们是平台独立的，这意味着您可以用高级语言编写程序，并在不同类型的机器上运行它。高级语言类似于英语，易于学习和使用。高级编程语言中的指令称为语句。例如，下面是一个计算半径为5的圆的面积的高级语言语句:
$$
\text { area }=5 * 5 * 3.1415
$$
有许多高级编程语言，每种语言都是为特定目的而设计的。表1.1列出了一些流行的。

用高级语言编写的程序称为源程序或源代码。因为计算机不能理解源程序，所以源程序必须翻译成机器码才能执行。翻译可以使用另一种称为解释器或编译器的编程工具来完成。

解释器从源代码中读取一条语句，将其转换为机器码或虚拟机代码，然后立即执行，如图1.9a所示。

An interpreter reads one statement from the source code, translates it to the machine code or virtual machine code, and then executes it right away, as shown in Figure 1.9a.

Note that a statement from the source code may be translated into several machine instructions.

A compiler translates the entire source code into a machine-code file, and the machine-code file is then executed, as shown in Figure 1.9b.

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|机器学习代写machine learning代考|STAT3888

Posted on 2023年8月16日2023年8月28日 by statistics-lab

如果你也在怎样代写机器学习Machine Learning 这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。机器学习Machine Learning令人兴奋。这是有趣的，具有挑战性的，创造性的，和智力刺激。它还为公司赚钱，自主处理大量任务，并从那些宁愿做其他事情的人那里消除单调工作的繁重任务。

机器学习Machine Learning也非常复杂。从数千种算法、数百种开放源码包，以及需要具备从数据工程(DE)到高级统计分析和可视化等各种技能的专业实践者，ML专业实践者所需的工作确实令人生畏。增加这种复杂性的是，需要能够与广泛的专家、主题专家(sme)和业务单元组进行跨功能工作——就正在解决的问题的性质和ml支持的解决方案的输出进行沟通和协作。

statistics-lab™ 为您的留学生涯保驾护航在代写机器学习 machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

计算机代写|机器学习代写machine learning代考|UCI Categorization

The classification results obtained for all the UCI data sets considering the different ECOC configurations are shown in Table 2.2. In order to compare the performances provided for each strategy, the table also shows the mean rank of each ECOC design considering the twelve different experiments. The rankings are obtained estimating each particular ranking $r_i^j$ for each problem $i$ and each ECOC configuration $j$, and computing the mean ranking $R$ for each design as $R_j=\frac{1}{N} \sum_i r_i^j$, where $N$ is the total number of data sets. We also show the mean number of classifiers (#) required for each strategy.

In order to analyze if the difference between ranks (and hence, the methods) is statistically significant, we apply a statistical test. In order to reject the null hypothesis (which implies no significant statistical difference among measured ranks and the mean rank), we use the Friedman test. The Friedman statistic value is computed as follows:
$$
X_F^2=\frac{12 N}{k(k+1)}\left[\sum_j R_j^2-\frac{k(k+1)^2}{4}\right] .
$$
In our case, with $k=4$ ECOC designs to compare, $X_F^2=-4.94$. Since this value is rather conservative, Iman and Davenport proposed a corrected statistic:
$$
F_F=\frac{(N-1) X_F^2}{N(k-1)-X_F^2}
$$

Applying this correction we obtain $F_F=-1.32$. With four methods and twelve experiments, $F_F$ is distributed according to the $F$ distribution with 3 and 33 degrees of freedom. The critical value of $F(3,33)$ for 0.05 is 2.89 . As the value of $F_F$ is no higher than 2.98 we can state that there is no statistically significant difference among the ECOC schemes. This means that all four strategies are suitable in order to deal with multi-class categorization problems. This result is very satisfactory and encourages the use of the compact approach since similar (or even better) results can be obtained with far less number of classifiers. Moreover, the GA evolutionary version of the compact design improves in the mean rank to the rest of classical coding strategies, and in most cases outperforms the binary compact approach in the present experiment. This result is expected since the evolutionary version looks for a compact ECOC matrix configuration that minimizes the error over the training data. In particular, the advantage of the evolutionary version over the binary one is more significant when the number of classes increases, since more compact matrices are available for optimization.

计算机代写|机器学习代写machine learning代考|Labelled Faces in the Wild Categorization

This dataset contains 13000 faces images taken directly from the web from over 1400 people. These images are not constrained in terms of pose, light, occlusions or any other relevant factor. For the purpose of this experiment we used a specific subset, taking only the categories which at least have four or more examples, having a total of 610 face categories. Finally, in order to extract relevant features from the images, we apply an Incremental Principal Component Analysis procedure [16], keeping $99.8 \%$ of the information. An example of face images is shown in Fig. 2.4.
The results in the first row of Table 2.3 show that the best performance is obtained by the Evolutionary GA and PBIL compact strategies. One important observation is that Evolutionary strategies outperform the classical one-versus-all approach, with far less number of classifiers (10 instead of 610). Note that in this case we omitted the one-vs-one strategy since it requires 185745 classifiers for discriminating 610 face categories.

For this second computer vision experiment, we use the video sequences obtained from the Mobile Mapping System of [1] to test the ECOC methodology on a real traffic sign categorization problem. In this system, the position and orientation of the different traffic signs are measured with video cameras fixed on a moving vehicle. The system has a stereo pair of calibrated cameras, which are synchronized with a GPS/INS system. The result of the acquisition step is a set of stereo-pairs of images with their position and orientation information. From this system, a set of 36 circular and triangular traffic sign classes are obtained. Some categories from this data set are shown in Fig. 2.5. The data set contains a total of 3481 samples of size $32 \times 32$, filtered using the Weickert anisotropic filter, masked to exclude the background pixels, and equalized to prevent the effects of illumination changes. These feature vectors are then projected into a 100 feature vector by means of PCA.

The classification results obtained when considering the different ECOC configurations are shown in the second row of Table 2.3. The ECOC designs obtain similar classification results with an accuracy of over $90 \%$. However, note that the compact methodologies use six times less classifiers than the one-versus-all and 105 less times classifiers than the one-versus-one approach, respectively.

机器学习代考

计算机代写|机器学习代写machine learning代考|UCI Categorization

考虑不同ECOC配置的所有UCI数据集的分类结果如表2.2所示。为了比较每种策略提供的性能，下表还显示了考虑到12种不同实验的每种ECOC设计的平均排名。通过估计每个问题$i$和每个ECOC配置$j$的每个特定排名$r_i^j$得到排名，并计算每个设计的平均排名$R$为$R_j=\frac{1}{N} \sum_i r_i^j$，其中$N$为数据集的总数。我们还展示了每种策略所需的分类器的平均数量(＃)。

为了分析等级之间的差异(以及方法之间的差异)是否具有统计显著性，我们应用了统计检验。为了拒绝零假设(这意味着测量秩和平均秩之间没有显著的统计差异)，我们使用弗里德曼检验。弗里德曼统计值计算公式如下:
$$
X_F^2=\frac{12 N}{k(k+1)}\left[\sum_j R_j^2-\frac{k(k+1)^2}{4}\right] .
$$
在我们的案例中，与$k=4$ ECOC设计进行比较，$X_F^2=-4.94$。由于这个值相当保守，Iman和Davenport提出了一个修正后的统计:
$$
F_F=\frac{(N-1) X_F^2}{N(k-1)-X_F^2}
$$

应用这个修正，我们得到$F_F=-1.32$。通过4种方法和12个实验，$F_F$按照$F$的3自由度和33自由度分布进行分布。$F(3,33)$对0.05的临界值为2.89。由于$F_F$的值不大于2.98，我们可以认为ECOC方案之间没有统计学上的显著差异。这意味着这四种策略都适用于处理多类分类问题。这个结果非常令人满意，并鼓励使用紧凑方法，因为使用更少的分类器可以获得类似(甚至更好)的结果。此外，遗传进化版本的紧凑设计在平均秩上优于其他经典编码策略，并且在大多数情况下优于本实验中的二进制紧凑方法。这个结果是预期的，因为进化版本寻找一个紧凑的ECOC矩阵配置，使训练数据上的误差最小化。特别是，当类的数量增加时，进化版本相对于二进制版本的优势更加显著，因为可以使用更紧凑的矩阵进行优化。

计算机代写|机器学习代写machine learning代考|Labelled Faces in the Wild Categorization

该数据集包含13000张直接从网络上取自1400多人的人脸图像。这些图像不受姿势、光线、遮挡或任何其他相关因素的限制。为了这个实验的目的，我们使用了一个特定的子集，只取至少有四个或更多例子的类别，总共有610个面部类别。最后，为了从图像中提取相关特征，我们应用增量主成分分析程序[16]，保留99.8%的信息。一个人脸图像的例子如图2.4所示。
表2.3第一行的结果表明，进化遗传算法和PBIL压缩策略的性能最好。一个重要的观察结果是，进化策略优于经典的“一对全”方法，它的分类器数量要少得多(10个而不是610个)。注意，在这种情况下，我们省略了一对一策略，因为它需要185745个分类器来区分610个人脸类别。

对于第二个计算机视觉实验，我们使用从[1]的移动地图系统获得的视频序列来测试ECOC方法在实际交通标志分类问题上的应用。在这个系统中，不同的交通标志的位置和方向是通过固定在移动车辆上的摄像机来测量的。该系统有一对立体校准相机，与GPS/INS系统同步。采集步骤的结果是一组具有位置和方向信息的立体图像对。从这个系统中，得到了一组36个圆形和三角形交通标志类。该数据集中的一些类别如图2.5所示。该数据集共包含3481个大小为$32 × 32$的样本，使用Weickert各向异性滤波器进行滤波，屏蔽以排除背景像素，并进行均衡以防止光照变化的影响。然后通过PCA将这些特征向量投影成100个特征向量。

考虑不同ECOC配置得到的分类结果如表2.3第二行所示。ECOC设计获得了类似的分类结果，准确率超过90%。但是，请注意，紧凑方法使用的分类器比单对全方法少6倍，比单对一方法少105倍。

计算机代写|机器学习代写machine learning代考请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|机器学习代写machine learning代考|QBUS3820

Posted on 2023年8月16日2023年8月28日 by statistics-lab

计算机代写|机器学习代写machine learning代考|Evolutionary Compact Parametrization

When defining a compact design of an ECOC, the possible loss of generalization performance has to be taken into account. In order to deal with this problem an evolutionary optimization process is used to find a compact ECOC with high generalization capability.

In order to show the parametrization complexity of the compact ECOC design, we first provide an estimation of the number of different possible ECOC matrices that we can build, and therefore, the search space cardinality. We approximate this number using some simple combinatorial principles. First of all, if we have an $N$-class problem and $B$ possible bits to represent all the classes, we have a set $C W$ with $2^B$ different words. In order to build an ECOC matrix, we select $N$ codewords from $C W$ without replacement. In combinatorics this is represented as $\left(\begin{array}{c}2_N^B \ N\end{array}\right)$, which means that we can construct $V_{2^B}^N=\frac{2^{B} !}{\left(2^B-N\right) !}$ different ECOC matrices. Nevertheless, in the ECOC framework, one matrix and its opposite (swapping all zeros by ones and vice-versa) are considered as the same matrix, since both represent the same partitions of the data. Therefore, the approximated number of possible ECOC matrices with the minimum number of classifiers is $\frac{V_{2 B}^N}{2}=\frac{2^{B} !}{2\left(2^B-N\right) !}$. In addition to the huge cardinality, it is easy to show that this space is neither continuous nor differentiable, because a change in just one bit of the matrix may produce a wrong coding design.

In this type of scenarios, evolutionary approaches are often introduced with good results. Evolutionary algorithms are a wide family of methods that are inspired on the Darwin’s evolution theory, and used to be formulated as optimization processes where the solution space is neither differentiable nor well defined. In these cases, the simulation of natural evolution process using computers results in stochastic optimization techniques which often outperform classical methods of optimization when applied to difficult real-world problems. Although the most used and studied evolutionary algorithms are the Genetic Algorithms (GA), from the publication of the Population Based Incremental Learning (PBIL) in 1995 by Baluja and Caruana [4], a new family of evolutionary methods is striving to find a place in this field. In contrast to $\mathrm{GA}$, those new algorithms consider each value in the chromosome as a random variable, and their goal is to learn a probability model to describe the characteristics of good individuals. In the case of PBIL, if a binary chromosome is used, a uniform distribution is learned in order to estimate the probability of each variable to be one or zero.

In this chapter, we report experiments made with the selected evolutionary strategies – i.e. GA and PBIL. Note that for both Evolutionary Strategies, the encoding step and the adaptation function are exactly equivalent.

计算机代写|机器学习代写machine learning代考|Problem encoding

Problem encoding: The first step in order to use an evolutionary algorithm is to define the problem encoding, which consists of the representation of a certain solution or point in the search space by means of a genotype or alternatively a chromosome [14]. When the solutions or individuals are transformed in order to be represented in a chromosome, the original values (the individuals) are referred as phenotypes, and each one of the possible settings for a phenotype is the allele. Binary encoding is the most common, mainly because the first works about GA used this type of encoding. In binary encoding, every chromosome is a string of bits. Although this encoding is often not natural for many problems and sometimes corrections must be performed after crossover and/or mutation, in our case, the chromosomes represent binary ECOC matrices, and therefore, this encoding perfectly adapts to the problem. Each ECOC is encoded as a binary chromosome $\zeta=$, where $h_i^{c_j} \in{0,1}$ is the expected value of the $i$-th classifier for the class $c_j$, which corresponds to the $i-t h$ bit of the class $c_j$ codeword.

Adaptation function: Once the encoding is defined, we need to define the adaptation function, which associates to each individual its adaptation value to the environment, and thus, their survival probability. In the case of the ECOC framework, the adaptation value must be related to the classification error.

Given a chromosome $\zeta=\left\langle\zeta_0, \zeta_1, \ldots, \zeta_L>\right.$ with $\zeta_i \in{0,1}$, the first step is to recover the ECOC matrix $M$ codified in this chromosome. The elements of $M$ allow to create binary classification problems from the original multi-class problem, following the partitions defined by the ECOC columns. Each binary problem is addressed by means of a binary classifier, which is trained in order to separate both partitions of classes. Assuming that there exists a function $y=f(x)$ that maps each sample $x$ to its real label $y$, training a classifier consists of finding the best parameters $w^$ of a certain function $y=f^{\prime}(x, w)$, in the manner that for any other $w \neq w^, f^{\prime}\left(x, w^\right)$ is a better approximation to $f$ than $f^{\prime}(x, w)$. Once the $w^$ are estimated for each binary problem, the adaptation value corresponds to the classification error. In order to take into account the generalization power of the trained classifiers, the estimation of $w^*$ is performed over a subset of the samples, while the rest of the samples are reserved for a validation set, and the adaptation value $\xi$ is the classification error over that validation subset. The adaptation value for an individual represented by a certain chromosome $\zeta_i$ can be formulated as:
$$
\varepsilon_i\left(P, Y, M_i\right)=\frac{\sum_{j=1}^s \delta\left(\rho_j, M_i\right) \neq y_j}{s},
$$
where $M_i$ is the ECOC matrix encoded in $\zeta_i, P=\left\langle\rho_1, \ldots, \rho_s\right\rangle$ a set of samples, $Y=\left\langle y_1, \ldots, y_s\right\rangle$ the expected labels for samples in $P$, and $\delta$ is the function that returns the classification label applying the decoding strategy.

机器学习代考

计算机代写|机器学习代写machine learning代考|Evolutionary Compact Parametrization

在定义ECOC的紧凑设计时，必须考虑到可能的泛化性能损失。为了解决这一问题，采用进化优化方法寻找具有高泛化能力的紧凑ECOC。

为了显示紧凑ECOC设计的参数化复杂性，我们首先提供了我们可以构建的不同可能ECOC矩阵的数量的估计，从而提供了搜索空间基数。我们用一些简单的组合原理来近似这个数字。首先，如果我们有一个$N$ -class问题，并且有$B$个可能的位来表示所有的class，那么我们就有一个包含$2^B$个不同单词的集合$C W$。为了构建ECOC矩阵，我们从$C W$中选择$N$码字而不进行替换。在组合学中，这表示为$\left(\begin{array}{c}2_N^B \ N\end{array}\right)$，这意味着我们可以构造$V_{2^B}^N=\frac{2^{B} !}{\left(2^B-N\right) !}$不同的ECOC矩阵。然而，在ECOC框架中，一个矩阵和它的对立面(用1交换所有零，反之亦然)被认为是相同的矩阵，因为两者都表示数据的相同分区。因此，具有最小分类器数的可能ECOC矩阵的近似值为$\frac{V_{2 B}^N}{2}=\frac{2^{B} !}{2\left(2^B-N\right) !}$。除了巨大的基数之外，很容易表明这个空间既不是连续的也不是可微的，因为仅仅改变矩阵的一位就可能产生错误的编码设计。

在这种类型的场景中，引入进化方法通常会带来良好的结果。进化算法是受达尔文进化论启发的一大类方法，过去常被表述为求解空间既不可微也不能很好定义的优化过程。在这些情况下，使用计算机模拟自然进化过程的结果是随机优化技术，当应用于困难的现实世界问题时，这种技术通常优于经典的优化方法。虽然使用和研究最多的进化算法是遗传算法(Genetic algorithms, GA)，但从1995年Baluja和Caruana[4]发表的基于种群的增量学习(Population Based Incremental Learning, PBIL)开始，一个新的进化方法家族正在努力在这一领域找到一席之地。与$\mathrm{GA}$相比，这些新算法将染色体中的每个值视为随机变量，其目标是学习一个概率模型来描述优秀个体的特征。在PBIL的情况下，如果使用双染色体，则学习均匀分布以估计每个变量为1或0的概率。

在本章中，我们报告了用选择的进化策略-即GA和PBIL进行的实验。注意，对于两种进化策略，编码步骤和适应函数是完全相同的。

计算机代写|机器学习代写machine learning代考|Problem encoding

问题编码:使用进化算法的第一步是定义问题编码，问题编码包括通过基因型或染色体在搜索空间中表示某个解或点[14]。当溶液或个体被转化以在染色体中表示时，原始值(个体)被称为表型，而表型的每个可能设置都是等位基因。二进制编码是最常见的，主要是因为关于GA的第一个作品使用了这种类型的编码。在二进制编码中，每条染色体都是一串比特。虽然这种编码对于许多问题来说往往是不自然的，有时在交叉和/或突变之后必须进行修正，但在我们的例子中，染色体代表二进制ECOC矩阵，因此，这种编码完美地适应了问题。每个ECOC被编码为一个二进制染色体$\zeta=$，其中$h_i^{c_j} \in{0,1}$是类$c_j$的$i$ -第一个分类器的期望值，它对应于类$c_j$码字的$i-t h$位。

适应函数:一旦编码被定义，我们需要定义适应函数，它与每个个体对环境的适应值相关联，从而与他们的生存概率相关联。对于ECOC框架，自适应值必须与分类误差相关。

给定一条含有$\zeta_i \in{0,1}$的染色体$\zeta=\left\langle\zeta_0, \zeta_1, \ldots, \zeta_L>\right.$，第一步是恢复在该染色体中编码的ECOC矩阵$M$。$M$的元素允许根据ECOC列定义的分区，从原始的多类问题创建二元分类问题。每个二进制问题都是通过一个二进制分类器来解决的，该分类器是为了分离类的两个分区而训练的。假设存在一个函数$y=f(x)$，它将每个样本$x$映射到它的真实标签$y$，那么训练一个分类器就是找到某个函数$y=f^{\prime}(x, w)$的最佳参数$w^$，因为对于任何其他的$w \neq w^, f^{\prime}\left(x, w^\right)$都比$f^{\prime}(x, w)$更接近$f$。一旦对每个二值问题估计出$w^$，其自适应值就对应于分类误差。为了考虑训练的分类器的泛化能力，对样本的一个子集执行$w^*$的估计，而其余的样本保留给一个验证集，并且自适应值$\xi$是该验证子集上的分类误差。以某条染色体$\zeta_i$为代表的个体的适应值可表示为:
$$
\varepsilon_i\left(P, Y, M_i\right)=\frac{\sum_{j=1}^s \delta\left(\rho_j, M_i\right) \neq y_j}{s},
$$
其中$M_i$是在$\zeta_i, P=\left\langle\rho_1, \ldots, \rho_s\right\rangle$中编码的ECOC矩阵(一组样本)，$Y=\left\langle y_1, \ldots, y_s\right\rangle$是$P$中样本的期望标签，$\delta$是应用解码策略返回分类标签的函数。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|机器学习代写machine learning代考|MKTG6010

Posted on 2023年8月16日2023年8月28日 by statistics-lab

计算机代写|机器学习代写machine learning代考|Local Binary Patterns

The local binary pattern (LBP) operator [14] is a powerful $2 \mathrm{D}$ texture descriptor that has the benefit of being somewhat insensitive to variations in the lighting and orientation of an image. The method has been successfully applied to applications such as face recognition [1] and facial expression recognition [16]. As illustrated in Fig. 1.2, the LBP algorithm associates each interior pixel of an intensity image with a binary code number in the range $0-256$. This code number is generated by taking the surrounding pixels and, working in a clockwise direction from the top left hand corner, assigning a bit value of 0 where the neighbouring pixel intensity is less than that of the central pixel and 1 otherwise. The concatenation of these bits produces an eight-digit binary code word which becomes the grey-scale value of the corresponding pixel in the transformed image. Figure 1.2 shows a pixel being compared with its immediate neighbours. It is however also possible to compare a pixel with others which are separated by distances of two, three or more pixel widths, giving rise to a series of transformed images. Each such image is generated using a different radius for the circularly symmetric neighbourhood over which the LBP code is calculated.

Another possible refinement is to obtain a finer angular resolution by using more than 8 bits in the code-word [14]. Note that the choice of the top left hand corner as a reference point is arbitrary and that different choices would lead to different LBP codes; valid comparisons can be made, however, provided that the same choice of reference point is made for all pixels in all images.

It is noted in [14] that in practice the majority of LBP codes consist of a concatenation of at most three consecutive sub-strings of $0 \mathrm{~s}$ and $1 \mathrm{~s}$; this means that when the circular neighbourhood of the centre pixel is traversed, the result is either all $0 \mathrm{~s}$, all $1 \mathrm{~s}$ or a starting point can be found which produces a sequence of 0 s followed by a sequence of $1 \mathrm{~s}$. These codes are referred to as uniform patterns and, for an 8 bit code, there are 58 possible values. Uniform patterns are most useful for texture discrimination purposes as they represent local micro-features such as bright spots, flat spots and edges; non-uniform patterns tend to be a source of noise and can therefore usefully be mapped to the single common value 59 .

In order to use LBP codes as a face expression comparison mechanism it is first necessary to subdivide a face image into a number of sub-windows and then compute the occurrence histograms of the LBP codes over these regions. These histograms can be combined to generate useful features, for example by concatenating them or by comparing corresponding histograms from two images.

计算机代写|机器学习代写machine learning代考|Fast Correlation-Based Filtering

Broadly speaking, feature selection algorithms can be divided into two groups: wrapper methods and filter methods [3]. In the wrapper approach different combinations of features are considered and a classifier is trained on each combination to determine which is the most effective. Whilst this approach undoubtedly gives good results, the computational demands that it imposes render it impractical when a very large number of features needs to be considered. In such cases the filter approach may be used; this considers the merits of features in themselves without reference to any particular classification method.

Fast correlation-based filtering (FCBF) has proved itself to be a successful feature selection method that can handle large numbers of features in a computationally efficient way. It works by considering the classification between each feature and the class label and between each pair of features. As a measure of classification the concept of symmetric uncertainty is used; for a pair random variables $X$ and $Y$ this is defined as:
$$
S U(X, Y)=2\left[\frac{I G(X, Y)}{H(X)+H(Y)}\right]
$$
where $H(\cdot)$ is the entropy of the random variable and $I G(X, Y)=H(X)-H(X \mid Y)=$ $H(Y)-H(Y \mid X)$ is the information gain between $X$ and $Y$. As its name suggests, symmetric uncertainty is symmetric in its arguments; it takes values in the range $[0,1]$ where 0 implies independence between the random variables and 1 implies that the value of each variable completely predicts the value of the other. In calculating the entropies of Eq. 1.6, any continuous features must first be discretised.

The FCBF algorithm applies heuristic principles that aim to achieve a balance between using relevant features and avoiding redundant features. It does this by selecting features $f$ that satisfy the following properties:

$S U(f, c) \geq \delta$ where $c$ is the class label and $\delta$ is a threshold value chosen to suit the application.
$\forall g: S U(f, g) \geq S U(f, c) \Rightarrow S U(f, c) \geq S U(g, c)$ where $g$ is any feature other than $f$.

Here, property 1 ensures that the selected features are relevant, in that they are correlated with the class label to some degree, and property 2 eliminates redundant features by discarding those that are strongly correlated with a more relevant feature.

机器学习代考

计算机代写|机器学习代写machine learning代考|Local Binary Patterns

局部二元模式(LBP)算子[14]是一种功能强大的$2 \mathrm{D}$纹理描述符，其优点是对图像的光照和方向变化不敏感。该方法已成功应用于人脸识别[1]、面部表情识别[16]等应用。如图1.2所示，LBP算法将强度图像的每个内部像素与范围为$0-256$的二进制码数相关联。这个代码号是通过取周围的像素，从左上角开始顺时针方向工作，在邻近像素强度小于中心像素时分配位值0，否则分配位值1来生成的。这些位的连接产生一个8位二进制码字，它成为转换后的图像中相应像素的灰度值。图1.2显示了一个像素与其近邻的比较。然而，也可以将一个像素与被两个、三个或更多像素宽度的距离隔开的其他像素进行比较，从而产生一系列转换后的图像。每个这样的图像都是使用不同半径的圆对称邻域来生成的，LBP代码是在这个邻域上计算的。

另一种可能的改进是通过在码字中使用超过8位来获得更精细的角度分辨率[14]。注意，左上角作为参考点的选择是任意的，不同的选择将导致不同的LBP代码;然而，只要对所有图像中的所有像素选择相同的参考点，就可以进行有效的比较。

在[14]中指出，在实践中，大多数LBP码由最多三个连续的$0 \mathrm{~s}$和$1 \mathrm{~s}$子串组成;这意味着当遍历中心像素的圆形邻域时，结果要么是全部$0 \mathrm{~s}$，全部$1 \mathrm{~s}$，要么可以找到一个起点，它产生一个0 s序列，后面是一个$1 \mathrm{~s}$序列。这些代码被称为统一模式，对于一个8位的代码，有58个可能的值。均匀的图案在纹理识别中最有用，因为它们代表了局部的微特征，如亮点、平斑和边缘;不均匀的模式往往是噪声源，因此可以有效地映射到单一的公共值59。

为了使用LBP码作为人脸表情比较机制，首先需要将人脸图像细分为多个子窗口，然后计算这些区域上LBP码的出现直方图。这些直方图可以组合起来生成有用的特征，例如通过连接它们或比较来自两幅图像的相应直方图。

计算机代写|机器学习代写machine learning代考|Fast Correlation-Based Filtering

包装器方法考虑了不同的特征组合，并在每种组合上训练分类器，以确定哪种组合最有效。虽然这种方法无疑给出了很好的结果，但当需要考虑非常大量的特征时，它所施加的计算需求使其不切实际。在这种情况下，可以使用过滤器方法;这考虑了特征本身的优点，而不参考任何特定的分类方法。

快速相关滤波(Fast correlation-based filtering, FCBF)是一种成功的特征选择方法，能够以高效的计算方式处理大量特征。它通过考虑每个特征与类标号之间以及每对特征之间的分类来工作。作为分类的度量，对称不确定性的概念被使用;对于一对随机变量$X$和$Y$，定义为:
$$
S U(X, Y)=2\left[\frac{I G(X, Y)}{H(X)+H(Y)}\right]
$$
其中$H(\cdot)$为随机变量的熵，$I G(X, Y)=H(X)-H(X \mid Y)=$$H(Y)-H(Y \mid X)$为$X$与$Y$之间的信息增益。顾名思义，对称不确定性在其参数中是对称的;它的取值范围为$[0,1]$，其中0表示随机变量之间的独立性，1表示每个变量的值完全预测另一个变量的值。在计算Eq. 1.6的熵时，必须首先对任何连续特征进行离散。

FCBF算法采用启发式原则，目的是在使用相关特征和避免冗余特征之间取得平衡。它通过选择满足以下属性的功能$f$来实现这一点:

$S U(f, c) \geq \delta$ 其中$c$是类标签，$\delta$是为适应应用程序而选择的阈值。

$\forall g: S U(f, g) \geq S U(f, c) \Rightarrow S U(f, c) \geq S U(g, c)$ 其中$g$是除$f$之外的任何特性。

在这里，属性1确保所选择的特征是相关的，因为它们在某种程度上与类标签相关，而属性2通过丢弃那些与更相关的特征强烈相关的特征来消除冗余特征

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|数据库作业代写Database代考|CSC453

Posted on 2023年8月15日2023年8月28日 by statistics-lab

如果你也在怎样代写数据库Database 这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。数据库Database可以成为一种强大的工具，它可以做计算机程序最擅长的事情:存储、操作和显示数据。

数据库Database不仅在许多应用程序中发挥作用，而且经常发挥关键作用。如果数据没有正确存储，它可能会损坏，程序将无法有意义地使用它。如果数据组织不当，程序可能无法在合理的时间内找到所需的数据。
除非数据库安全有效地存储其数据，否则无论系统的其余部分设计得多么好，应用程序都将是无用的。

statistics-lab™ 为您的留学生涯保驾护航在代写数据库Database方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据库Database代写方面经验极为丰富，各种代写数据库Database相关的作业也就用不着说。

计算机代写|数据库作业代写Database代考|TEMPORAL DATA

As its name implies, temporal data involves time. For example, suppose you sell produce and the prices vary greatly from month to month. (For instance, tomatoes are expensive in the winter, while pumpkins are practically worthless on November 1.) To keep a complete record of your sales, you need to track not only orders but also the prices at the time each order was placed.
The following sections describe a few time-related database design issues.
Effective Dates
One simple way to track an object that changes over time is to add fields to the object giving its valid dates. Those fields give the object’s effective or valid dates.

Figure 9.26 shows a relational model for temporal produce orders or orders for any other products with prices that change over time.

The Orders table contains an OrderId field and a Date, in addition to other order information such as CustomerId. The OrderId field provides the link to the OrderItems table.

Each OrderItems record represents one line item in an order. Its ProductId field provides a link to the Products table, which describes the product purchased on this line item. The Quantity field tells the number of items purchased.

The ProductPrices table has a ProductId field that refers back to the Products table. The Price field gives the product’s price. The EffectiveStartDate and EffectiveEndDate fields tell when that price was in effect.
To reproduce an order, you would follow these steps:

Look up the order in the Orders table and get its OrderId. Record the OrderDate.
Find the OrderItems records with that OrderId. For each of those records, record the Quantity and ProductId. Then:
a. Find the Products record with that ProductId. Use this record to get the item’s description.
b. Find the ProductPrices record with that ProductId and where EffectiveStartDate $\leq$ OrderDate $\leq$ EffectiveEndDate. Use this record to get the item’s price at the time the order was placed.

计算机代写|数据库作业代写Database代考|Deleted Objects

When you delete a record, the information that the record used to hold is gone forever. If you delete an employee’s records, you lose all of the information about that employee, including the fact that he was fired for selling your company’s secrets to the competition. Because the employee’s records were deleted, they could potentially get a job in another part of the company and resume spying with no one the wiser.

Similarly when you modify a record, its previous values are lost. Sometimes that doesn’t matter, but other times it might be worthwhile to keep the old values around for historical purposes. For example, it might be nice to know that an employee’s salary was increased by only 0.25 percent last year, so you might consider a bigger increase this year.

One way to keep all of this data is to never, ever delete or modify records. Instead, you use effective dates to “end” the old record. If you’re modifying the record rather than deleting it, you would then create a new record with effective dates starting now.
For example, suppose you hired Hubert Phreen on $4 / 1 / 2027$ for a salary of $\$ 45,000$. On his first anniversary, you increased his salary to $\$ 46,000$ and on his second you increased it to $\$ 53,000$. He then grew spoiled and lazy, so he hasn’t gotten a raise since. The following table shows the records this scenario would generate in the EmployeeSalaries table. Using this data, you can tell what Hubert’s current salary is and what it was at any past point in time.

数据库代考

计算机代写|数据库作业代写Database代考|TEMPORAL DATA

顾名思义，时间数据涉及时间。例如，假设你出售农产品，每个月的价格变化很大。(例如，西红柿在冬天很贵，而南瓜在11月1日几乎一文不值。)为了保持完整的销售记录，您不仅需要跟踪订单，还需要跟踪每笔订单下单时的价格。
下面几节描述几个与时间相关的数据库设计问题。
生效日期
跟踪随时间变化的对象的一种简单方法是向对象添加字段，给出其有效日期。这些字段给出对象的有效或有效日期。

图9.26显示了临时产品订单的关系模型，或价格随时间变化的任何其他产品的订单。

Orders表除了包含CustomerId等其他订单信息外，还包含OrderId字段和Date。OrderId字段提供到OrderItems表的链接。

每个OrderItems记录表示订单中的一行项。它的ProductId字段提供了到Products表的链接，该表描述了在此行项上购买的产品。Quantity字段表示购买的物品数量。

ProductPrices表有一个ProductId字段，它指向Products表。Price字段给出产品的价格。effecvestartdate和effecveenddate字段告诉我们价格何时生效。
要复制订单，您将遵循以下步骤:

在Orders表中查找订单并获取其OrderId。记录OrderDate。

查找具有该OrderId的OrderItems记录。对于每一个记录，记录Quantity和ProductId。然后:
a.查找ProductId对应的产品记录。使用此记录获取该项目的描述。
b.查找ProductPrices记录，其中ProductId和EffectiveStartDate $\leq$ OrderDate $\leq$ EffectiveEndDate。使用此记录可获得下订单时该商品的价格。

计算机代写|数据库作业代写Database代考|Deleted Objects

当您删除一条记录时，该记录曾经保存的信息将永远消失。如果你删除了一个员工的记录，你就失去了关于这个员工的所有信息，包括他因为把你公司的机密卖给竞争对手而被解雇的事实。因为员工的记录被删除了，他们可能会在公司的其他部门找到一份工作，并在没有人知道的情况下继续从事间谍活动。

类似地，当您修改一条记录时，它以前的值将丢失。有时这并不重要，但有时出于历史目的保留旧的价值观可能是值得的。例如，你可能很高兴知道某位员工去年的工资只增加了0.25％，所以你可能会考虑今年增加更多的工资。

保存所有这些数据的一种方法是永远不要删除或修改记录。相反，您可以使用有效日期来“结束”旧记录。如果您正在修改记录而不是删除它，那么您将创建一个具有从现在开始生效日期的新记录。
例如，假设你在$4 / 1 / 2027$上以$\$ 45,000$的薪水雇佣了Hubert Phreen。在他结婚一周年时，你把他的工资提高到$\$ 46,000$，在他结婚二周年时，你把工资提高到$\$ 53,000$。后来他被宠坏了，变得懒惰，所以从那以后他就没有加薪。下表显示了该场景将在EmployeeSalaries表中生成的记录。利用这些数据，你可以知道休伯特目前的工资是多少，以及过去任何一个时间点的工资是多少。

计算机代写|数据库作业代写Database代考请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|数据库作业代写Database代考|COMP421

Posted on 2023年8月15日2023年8月28日 by statistics-lab

计算机代写|数据库作业代写Database代考|Repeated Attribute Associations

Some entities have multiple fields that represent either the same kind of data or a very similar kind of data. For example, it is common for purchase orders and other documents to allow you to specify a daytime phone number and an evening phone number. Other contact-related records might allow you to specify even more phone numbers for such things as cellphones, emergency contacts, fax numbers, pagers, and others.
Figure 9.10 shows a semantic object model for a PERSON class that allows any number of Phone attributes.

In this model, we want to allow any number of instances of the repeated attribute even if they have the same type. For example, we would allow an order to include multiple emergency contact phone numbers.

To allow any number of repeated attributes in a relational model, build a new table to contain the repeated values. Use the original table’s primary key to link the new records back to the original table. Figure 9.11 shows how to do this for the PERSON class shown in Figure 9.10.

Because the Phones table’s primary key includes all of the table’s fields, the combination of PersonId/ Type/Number must be unique. That means a person can only use a phone number for a particular purpose once. That makes sense. It would be silly to list the same phone number as a work number twice for the same person. However, a person could have the same number for multiple purposes (daytime and evening numbers are the same cell phone) or have multiple phone numbers for the same purpose (office and receptionist numbers for work phone).

You can use the primary keys and other keys to enforce other kinds of uniqueness. For example, to prevent someone from using the same number for different purposes, make PersonId/Number a unique key. To prevent someone from providing more than one number for the same purpose (for example, two cell phone numbers), make PersonId/Type a unique key.

For another example, suppose you want to add multiple email addresses to the Persons table, allow each person to have any number of phone numbers and email addresses of any type, but not allow duplicate phone numbers or email addresses. (For example, you cannot use the same phone number for Home and Work numbers.)

计算机代写|数据库作业代写Database代考|Reflexive Associations

A reflexive association or recursive association is one in which an object refers to an object of the same class. You can use reflexive associations to model a variety of situations ranging from simple one-to-one relationships to complicated networks of association.
The following sections describe different kinds of reflexive associations.

One-to-One Reflexive Associations
As you can probably guess, in a one-to-one reflexive association an object refers to another single object of the same class. For example, consider the PERSON class’s Spouse field. A Person can be married to exactly one other person (at least in much of the world), so this is a one-to-one relationship. Figure 9.13 shows an ER diagram representing this relationship.

Figure 9.14 shows a relational model for this relationship.

Unfortunately, this design does not require that two spouses be married to each other. For example, Ann could be married to Bob and Bob could be married to Cindy. That might make an interesting television show, but it would make a confusing database.

数据库代考

计算机代写|数据库作业代写Database代考|Repeated Attribute Associations

有些实体有多个字段，这些字段表示相同类型的数据或非常相似的数据。例如，对于采购订单和其他文档，通常允许您指定白天电话号码和晚上电话号码。其他与联系人相关的记录可能允许您为手机、紧急联系人、传真号码、寻呼机等指定更多的电话号码。
图9.10显示了一个PERSON类的语义对象模型，它允许任意数量的Phone属性。

在这个模型中，我们希望允许重复属性的任意数量的实例，即使它们具有相同的类型。例如，我们允许一个订单包含多个紧急联系电话号码。

若要在关系模型中允许任意数量的重复属性，请构建一个包含重复值的新表。使用原始表的主键将新记录链接回原始表。图9.11显示了如何为图9.10中的PERSON类执行此操作。

因为Phones表的主键包含表的所有字段，所以PersonId/ Type/Number的组合必须是唯一的。这意味着一个人只能为特定目的使用一次电话号码。这很有道理。将同一个人的电话号码作为工作号码两次列出是愚蠢的。然而，一个人可以为多个目的使用同一个号码(白天和晚上的号码是同一个手机号码)，也可以为同一个目的使用多个电话号码(办公室和前台的电话号码是工作电话)。

您可以使用主键和其他键来强制其他类型的唯一性。例如，为了防止某人将同一号码用于不同目的，可以将PersonId/ number设置为唯一键。要防止某人为同一目的提供多个号码(例如，两个手机号码)，请将PersonId/Type设置为唯一键。

另一个例子，假设您想向Persons表添加多个电子邮件地址，允许每个人拥有任意数量的电话号码和任何类型的电子邮件地址，但不允许重复的电话号码或电子邮件地址。(例如，家庭电话和工作电话不能使用相同的电话号码。)

计算机代写|数据库作业代写Database代考|Reflexive Associations

自反关联或递归关联是指一个对象引用同一类的对象。您可以使用自反性关联来为各种情况建模，从简单的一对一关系到复杂的关联网络。
下面几节描述不同类型的反射联想。

一对一反身联想
正如您可能猜到的那样，在一对一的自反关联中，一个对象引用同一类的另一个对象。例如，考虑PERSON类的Spouse字段。一个人只能和另一个人结婚(至少在世界上的很多地方是这样)，所以这是一种一对一的关系。图9.13显示了表示这种关系的ER图。

图9.14显示了这个关系的关系模型。

不幸的是，这种设计并不要求夫妻双方必须结婚。例如，安可以嫁给鲍勃，鲍勃可以嫁给辛迪。这可能会成为一个有趣的电视节目，但它会成为一个令人困惑的数据库。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|数据库作业代写Database代考|CSCI4380

Posted on 2023年8月15日2023年8月28日 by statistics-lab

计算机代写|数据库作业代写Database代考|KEEP TABLES FOCUSED

If you keep each table well focused, then there will be fewer chances for misunderstandings. Developers will have an easier time keeping each table’s purpose straight, so different parts of the application will use the data in the same (intended) way.

Modern object-oriented programming languages also use objects and classes that are similar to the objects and classes used in semantic object modeling and that are similar to the entities and entity sets used by entity-relationship diagrams. If you do a good job of modeling and keep object and table definitions well focused, then those models practically write the code by themselves. There are still plenty of other things for the programmers to do, but at least they’ll be able to make programming object models that closely resemble the database structure.
USE THREE KINDS OF TABLES
One tip that can help you keep tables focused is to note that there are three basic kinds of tables. The first kind stores information about objects of a particular type such as Students, Employees, or QuarterlyReports. These hold the bulk of the application’s data.
The second kind of table represents a link between two or more objects. For example, the association objects described in Chapter 5, “Translating User Needs into Data Models,” represent a link between two types of objects.
Figure 8.1 shows a relational diagram to model employees and projects. Each employee can be assigned to multiple projects and each project can involve multiple employees. The EmployeeRoles table provides a link between the two object tables. It also stores additional information about the link. In this case, it stores the role that an employee played on each project.

The third basic kind of table is a lookup table. These are tables created simply to use in foreign key constraints. For example, the States table described in the earlier section “Convert Domains into Tables” is a lookup table.

When you build a table, ask yourself whether it represents an object, a link, or a lookup. If you cannot decide or if the table represents more than one of those, then the table’s purpose may not be clearly defined.

计算机代写|数据库作业代写Database代考|USE NAMING CONVENTIONS

Use consistent naming conventions when you name database objects. It doesn’t matter too much what conventions you use as long as you use something consistent.
Some database developers prefix table names with tbl and field names with fld as in, “The tblEmployees table contains the fldFirstName and fldLastName fields.” For simple databases, I prefer to omit the prefixes because it’s usually easy enough to tell which names represent objects (tables) and which represent attributes (fields).
Some developers also use a lnk prefix for tables that represent a link between objects as in, “The InkAnimalsPlanets table links tblAnimals and tblPlanets.” These developers are also likely to use lu or lup as a prefix for lookup tables.

Some developers prefer to use UPPERCASE_LETTERS for table names and lowercase_letters for field names. Others use MixedCase for both.
WARNING Some database engines might have their own views on names. For example, due to reasons that I’ll explain in Chapter 18, “PostgreSQL in Python,” PostgreSQL works most easily with names that are in lowercase.
Do a little research and perhaps write a test program or two before you define your naming conventions.
Some prefer to make table names singular (the Employee table) and others prefer to make them plural (the Employees table).
As I said, it doesn’t matter too much which of these conventions you use as long as you pick some conventions and stick to them.

However, there are three “mandatory” naming rules that you should follow or the other developers will snicker behind your back and openly mock you at parties.

数据库代考

计算机代写|数据库作业代写Database代考|KEEP TABLES FOCUSED

如果你把每一张桌子都放在重点上，那么误解的机会就会减少。开发人员将更容易保持每个表的用途，因此应用程序的不同部分将以相同的(预期的)方式使用数据。

现代面向对象编程语言也使用类似于语义对象建模中使用的对象和类，类似于实体关系图中使用的实体和实体集的对象和类。如果您在建模方面做得很好，并保持对象和表定义的重点，那么这些模型实际上可以自己编写代码。程序员还有很多其他的事情要做，但至少他们能够制作与数据库结构非常相似的编程对象模型。
使用三种桌子
可以帮助您保持表的重点的一个技巧是注意有三种基本类型的表。第一类存储特定类型对象的信息，如学生、雇员或季报。这些存储了应用程序的大部分数据。
第二类表表示两个或多个对象之间的链接。例如，第5章“将用户需求转换为数据模型”中描述的关联对象表示两种类型对象之间的链接。
图8.1显示了为员工和项目建模的关系图。每个员工可以分配到多个项目，每个项目可以涉及多个员工。EmployeeRoles表提供了两个对象表之间的链接。它还存储有关链接的其他信息。在本例中，它存储员工在每个项目中扮演的角色。

第三种基本类型的表是查找表。这些表只是为了在外键约束中使用而创建的。例如，前面章节“将域转换为表”中描述的States表是一个查找表。

在构建表时，要问自己它代表的是对象、链接还是查找。如果您不能确定，或者该表代表了其中一个以上，那么该表的目的可能没有明确定义。

计算机代写|数据库作业代写Database代考|USE NAMING CONVENTIONS

在命名数据库对象时使用一致的命名约定。只要使用一致的约定，使用什么约定并不重要。
一些数据库开发人员在表名前加上tbl，在字段名前加上field，如“雇员表包含fldFirstName和fldLastName字段”。对于简单的数据库，我倾向于省略前缀，因为通常很容易分辨哪些名称代表对象(表)，哪些代表属性(字段)。
一些开发人员还对表示对象之间链接的表使用link前缀，如“InkAnimalsPlanets表链接了tblAnimals和tblPlanets”。这些开发人员还可能使用lu或lup作为查找表的前缀。

一些开发人员更喜欢使用UPPERCASE_LETTERS作为表名，而使用lowercase_letters作为字段名。其他人则同时使用MixedCase。
某些数据库引擎可能对名称有自己的看法。例如，由于我将在第18章“Python中的PostgreSQL”中解释的原因，PostgreSQL最容易使用小写的名称。
在定义命名约定之前，做一些研究，或者编写一两个测试程序。
有些人喜欢用单数表名(Employee表)，有些人喜欢用复数表名(Employees表)。
正如我所说的，只要你选择一些约定并坚持下去，你使用这些约定中的哪一个并不重要。

然而，你必须遵守三个“强制性”的命名规则，否则其他开发者会在背后嘲笑你，并在聚会上公开嘲笑你。

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