### cs代写|机器学习代写machine learning代考|Scientific programming with Python

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• 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

## cs代写|机器学习代写machine learning代考|Basic language elements

As a general purpose programming language, Python contains basic programing concepts such as basic data types, loops, conditional statements, and subroutines. We will briefly review the associated syntax with examples that are provided in file FirstProgram. ipynb. In addition to such basic programming constructs, all major programming languages such as Python are supported by a large number of libraries that enable a wide array of programming styles and specialized functions. We are here mainly interested in basic scientific computing, in contrast to system programming, and for this we need multidimensional arrays. We therefore base almost all programs in this book on the NumPy library. NumPy provides basic support of common scientific constructs and functions such as trigonometric functions and random number generators. Most importantly, it provides support for N-dimensional arrays. NumPy has become the standard in scientific computing with Python. We will use this wellestablished constructs to implement vectors, matrices and higher dimensional arrays. While there is a separate matrix class, this construct is limited to a two dimensional structure and has not gained widespread acceptance.

An established way to import the NumPy library in our programs is to map them to the name space “np” with the command import numpy as np. In this way, the specific methods or functions of NumPy are accessed with the prefix $\mathrm{np} .$ In addition to importing NumPy, we always import a plotting library as plotting results will be very useful and a common way to communicate results. We specifically use the popular PyPlot package of the Matploitlib library. Hence, we nearly always start our program with the two lines In the following, we walk through a program in the Jupyter environment called FirstProgram. These lines of code are intended to show the syntax of the basic programming constructs that we need in this book. We start by demonstrating the basic data types that we will be using frequently. We are mainly concerned with numerical data, of which a scalar is the simplest example, We here show the code as well as the response of running the program with the print () function. Comment lines can be included with the hash-tag symbol #. The type of the variables are dynamically assigned in Python. That is, a variable name and corresponding memory space is allocated the first time a variable with this name is used on the left hand side of an assignment operator ” $=$ “. In this case it is an interger value, but we could also assign a real-valued variable with textttaScalar=4.0.

## cs代写|机器学习代写machine learning代考|Functions

This book tries to use minimal examples that do not require advanced code structuring techniques such as object oriented-programming, although those techniques are available in Python. The basic code reuse technique is of course the definition of a function. In Python this can be done with the following template. To structure code better, specifically to define some code that can be reused, we have the option to define functions like

Simple variables are passed by value in Python, but more complex objects might be referred by reference. It is therefore wise to be careful when changing the content of calling variables in the functions. The function can be called with an argument, and we showed in the example how to provide a default argument.

It is also useful to define an inline version of a function, such as defining logistic sigmoid function We will use this inline function below to plot it.

## cs代写|机器学习代写machine learning代考|Code efficiency and vectorization

Machine learning is about working with large collections of data. Such data are kept in data bases, spreadsheets, or simply in text files, but to work with them we load them into arrays. Since we define operations on such arrays, it is better to treat these arrays as vectors, matrices, or generally as tensors. Traditional programming languages such as $\mathrm{C}$ and Fortran require us to write code that loops over all the indices in order to specify operations that are defined on all the data. For example, as provided in the program MatrixMultiplication.ipynb, let us define two random $n \times n$ matrices with the NumPy random number generator for uniformly distributed numbers,

It is now common to call this style of programming a vectorized code. Such a vectorized code is not only much easier to read, but it is also essential to write efficient code. The reason for this is that the system programmers can implement such routines very efficiently, and this is difficult to match with the more general but inefficient explicit index operation.

To demonstrate the efficiency issue, let us measure the time of operations for a matrix multiplication. We start as usual by importing the standard NumPy and Matplotlib libraries, and we also import a timer routine with We then define a method called matmulslow that implements a matrix multiplication with an explicit iteration over the indices.

## 有限元方法代写

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## 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。