### 统计代写|数据结构作业代写data structure代考|System-defined data types

statistics-lab™ 为您的留学生涯保驾护航 在代写数据结构data structure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结构data structure方面经验极为丰富，各种代写数据结构data structure相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|数据结构作业代写data structure代考|Abstract Data Types

Before definimg abstract data types, let us consider the different view of systen-defiacd data types. We all know that, by default, all primátive data types (int, float, etc.) support basic operations such as addition and subtraction. The systen provides the inplenentations for the primative data types. For user-defined data types we also need to define operations. The implenacntation for these operations can be done when we Want to actually use them. That mueans, in general, user defined data types are defined along with their operations.
To simplify the process of solving problens, we conbine the data structures with their operations and we call this Abstract Data Types (AD)’s). An Alyl consists of two parts

1. Declaration of data
2 Declaration of operations
Conmonly used ADTs include: Linked Lists, Stacks, Queues, Pronity Queues, Binary Trees, Dictionaries, Dijoint Sets (Union and Find), Hash Tables, Graphs, and many others. For cxample, stack uses L.IFO (Last-In-First-Out) mechanism while storing the dafa in data structurcs. The last element inserted into the stack is the first element that gets deleted. Common operations of it are: creating the stack, pushing an element outo the stack, popping an element from stack, finding the current top of the stack, finding number of elements in the stack, etc.
While defining the ADTs do not worry about the implementation details. They come into the picture only when we want to use them. Different kinds of ADT $\mathrm{s}$ are suiterl to different kinds of applications, and anme are highly sperialized to sperific task. Ry the end of this book, we will go through many of them and you will be in a position to relate the data structures to the kind of problens they solve.

## 统计代写|数据结构作业代写data structure代考|Types of Analysis

algorithn takes a bong tinc. We have ailready secn that ian ilgorithn can be represented in the form of in expression. That means we represent the stgorithin with multiple expressionst one for the case where it takes less tinio and another for the chse where it takes hwore tinie.

In general, the first case is called the best case and the second case is called the worst case for the algonthm. To analyze an algonthm, we need some kind of syatax, and that forms the base for asymptotic analysis/motation. There are three types of analysis:

• Worst crse
• Defines the imput for which the algorithm takes a long tinve (slowest time to complete). – Input is the one for which the algorithm runs the slowest.
• Bent case
• Defines the input for which the algorithm takes the least tinve (fastest time to complete).
• Input is the one for which the algorithm runs the fastest.
• Average case
• Prowides a prediction about the running time of the algorithm.
• Run the algorithm many times, using many different inputs that come from some distribution that generates these inputs, compute the total rmning tine (by adding the individual times), and divide by the number of trials. Assunes that the input is randon.
Lower Bound $<=$ Average Time $<=$ Upper Bound
For a given algorithm, we can represent the best, worst and average cases in the form of expressions. $A$ s an example, let $f(n)$ be the function which represents the given algorithm.
$$\begin{gathered} f(n)=n^{2}+500, \text { for worst case } \ f(n)=n+100 n+500 \text {, for best case } \end{gathered}$$
Similarly, for the average case. The expression defines the inputs with which the algorithm takes the average running time (or memory).

## 统计代写|数据结构作业代写data structure代考|Big-O Notation

This notafion gives the tight upper bouad of the given function. Gencrally, it is represented as $f(n)=()(g(n))$. That neans, at larger values of $n$, the upper bound of $f(n)$ is $g(n)$. For example, if $f(n)=n^{4}+100 n^{2}+10 n+50$ is the given algorithm, then $n^{4}$ is $g(n)$. That means $g(n)$ gives the maxinum rate of growth for $f(n)$ at larger values of $n$.

Let us see the $\mathrm{O}$-notation with a little more detail. $\mathrm{O}$-notation defined as $\mathrm{O}(g(n))=f f(n)$ : there exist positive constants $c$ and $n_{0}$ sasch that $0 \leq f(n) \leq c g(n)$ for all $\left.n \geq n_{0}\right} \cdot g(n)$ is an asynptotic tight upper bound for $f(n)$. Our objective is to give the snallest rate of growth $g(n)$ which is greater than or equal to the given algorithms” rate of growth $f(n)$.
Gearerally, we discard bower values of $n$. That maeans the rate of growth at lower values of $n$ is not important. In the figure, $n_{0}$ is the point from which we need to consuder the rate of growth for a given algorithm. Below $n_{0}$, the rate of growth could be different. $n_{0}$ is called threshold for the given function.

## 统计代写|数据结构作业代写data structure代考|Abstract Data Types

1. 数据
声明 2 操作
声明 常用的 ADT 包括：链表、堆栈、队列、Pronity 队列、二叉树、字典、双联集（并集和查找）、哈希表、图等等。例如，堆栈使用 L.IFO（后进先出）机制，同时将 dafa 存储在数据结构中。插入堆栈的最后一个元素是第一个被删除的元素。它的常见操作是：创建堆栈，将一个元素压出堆栈，从堆栈中弹出一个元素，查找当前堆栈顶部，查找堆栈中的元素数量等。

## 统计代写|数据结构作业代写data structure代考|Types of Analysis

• 最坏的情况
• 定义算法需要很长时间（完成时间最慢）的输入。– 输入是算法运行最慢的输入。
• 弯曲案例
• 定义算法花费最少的时间（最快完成时间）的输入。
• 输入是算法运行速度最快的输入。
• 平均情况
• 扩展关于算法运行时间的预测。
• 多次运行算法，使用来自生成这些输入的某个分布的许多不同输入，计算总 rmning tine（通过添加各个时间），然后除以试验次数。假设输入是随机的。
下限<=平均时间<=上限
对于给定的算法，我们可以用表达式的形式表示最好的、最坏的和平均的情况。一种举个例子，让F(n)是表示给定算法的函数。
F(n)=n2+500, 对于最坏的情况  F(n)=n+100n+500, 最好的情况
同样，对于平均情况。该表达式定义了算法采用平均运行时间（或内存）的输入。

## 统计代写|数据结构作业代写data structure代考|Big-O Notation

Gearerally，我们丢弃 Bower 值n. 这意味着在较低值下的增长率n并不重要。图中，n0是我们需要考虑给定算法的增长率的点。以下n0, 增长率可能不同。n0被称为给定函数的阈值。

## 广义线性模型代考

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## MATLAB代写

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