### cs代写|机器学习代写machine learning代考|Data handling

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。

• 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 plots of iris data

Since machine learning requires data, we are commonly faced with importing data from files. There are a variety of tools to handle specific file formats. The most basic one is to reading data from text files. We can then manipulate the data and plot them in a form which can help us to gain insights into the information we want to get from the data. We will discuss some classical machine learning examples. These data are now often included in the libraries so that it will save us some time. However, preparing data to be used in machine learning is a large part of applying machine learning in practice. The following examples are provided in the program HouseMNIST. ipynb.
We start here with the example of the well-known classification problem of iris flowers. The iris dataset was collected from a field on the same day at the Gaspé region of eastern Quebec in Canada. These data were first used by the famous British statistician Ronald Fisher in a 1936 paper. The data consist of 150 samples, 50 samples of each of 3 species of the iris flower called iris Setosa $(0)$, iris Versicolour (1), and iris Virginica (2). For our purpose, we usually simply give each class a label such as a number, as shown in the bracket after the flower names in this example.

The dataset is given on the book’s web page with three text files, named iris . data, feature_names. txt, and target_names.txt, to start practising data handling. These are basic text files and their contents can be inspected by loading them into an editor. We are now exploring these data with with the program iris.ipynb. The data file contains both the feature values and the class label, and we can load these data into a NumPy array with the NumPy functions loadtxt. Printing out the shape of the array reveals that there are 150 lines of data, 1 for each sample, and 5 columns. The first four values are the measured length and width of septals and pedals of the flowers. The last number is the class label. The following code separates this data array into feature matrix and a target vector for all the samples. We also show how text can be handled with the NumPy function genfromtxt.

## cs代写|机器学习代写machine learning代考|Image processing and convolutional filters

This section dives into some image processing concepts and reviews convolution operations that become important later in this book. It is therefore important to review this section well. Also, the discussion gives us the opportunity to practice Python programing a bit more.

We have already displayed gray-scale images that were given by 2-dimensional matrices where each component stands for a gray level of one pixel. In order to represent color images we just need now three channels that each stands for one primary colors, red (R), green (G), and blue (B). Such RGB images are represented in a tensor of $M \times N \times 3$, where $M$ and $N$ are the size of horizontal and vertical resolutions in pixels. Reading and displaying an image file is incorporated in the Matplotlib library, though there are also a variety of other packages that can be used. For example, given a test image such as motorbike.jpg from the book’s web page as shown in Fig. 2.8B, a program to read this image into an array and to plot it is

The shape function reveals that this image has a resolution of $600 \times 800$ pixels with three color channels.

A main application of machine learning is object recognition, and we will now give an example of how we could accomplish this with a filter that highlights specific features in an image. Let’s assume we are looking for a red spot of a certain size in a photograph. Lets say we are given a picture as an RGB image like that is shown in Fig.2.9A. The corresponding program to read this image into an array and to plot it is

creates a new red pixel resulting in the image shown in Fig. 2.9B. We use this image for the following discussion.

The red spot that we want to detect with the following program is the structure in the upper left and not the red pixel with coordinate $(6,5)$ that we just added by hand above. We added this red pixel to discuss how we can distinguish between the main red object we are looking for and other red objects in the picture. It is interesting to look at the red, green, and blue channels separately, as shown in Fig. $2.9 \mathrm{C}$. Each of these plots can be produced with a code as in the following example for the red channel.

The open-source series of libraries called scikit build on the NumPy and SciPy libraries for more domain-specific support. In this chapter we briefly introducing the scikit-learn library, or sklearn for short. This library started as a Google Summer of Code project by David Cournapeau and developed into an open source library which now provides a variety of well-established machine learning algorithms. These algorithms together with excellent documentation are available at $. The goal of this chapter is to show how to apply machine learning algorithms in a general setting using some classic methods. In particular, we will show how to apply three important machine learning algorithms, a support vector classifier (SVC), a random forest classifier (RFC), and a multilayer perceptron (MLP). While many of the methods studied later in this book go beyond these now classic methods, this does not mean that these methods are obsolete. Quite the contrary; many applications have limited amounts of data where some more data-hungry techniques such as deep learning might not work. Also, the algorithms discussed here are providing some form of baseline to discuss advanced methods like probabilistic reasoning and deep learning. Our aim here is to demonstrate that applying machine learning methods based on such machine learning libraries is not very difficult. It also provides us with an opportunity to discuss evaluation techniques that are very important in practice. An outline of the algorithms and a typical work flow provided by scikit-learn, or sklearn for short, is shown in Fig. 3.1. The machine learning methods are thereby divided into classification, regression, clustering, and dimensionality reduction. We will later discuss the ideas behind the corresponding algorithms, specifically in the second half of this chapter, though we start by treating the methods first as a blackbox. We specifically outline in this chapter a typical machine learning setting for classification. In some applications it is possible to achieve sufficient performance without much need of knowing exactly what these algorithms do, although we will later show that applying machine learning to more challenging cases and avoiding pitfalls requires some deeper understanding of the algorithms. Our aim for the later part of this book is therefore to look much deeper into the principles behind machine learning including probabilistic and deep learning methods. ## 机器学习代写 ## cs代写|机器学习代写machine learning代考|Basic plots of iris data 由于机器学习需要数据，我们通常会面临从文件中导入数据的问题。有多种工具可以处理特定的文件格式。最基本的一种是从文本文件中读取数据。然后，我们可以操纵数据并以某种形式绘制它们，这可以帮助我们深入了解我们想从数据中获得的信息。我们将讨论一些经典的机器学习示例。这些数据现在通常包含在库中，这样可以节省我们一些时间。然而，准备用于机器学习的数据是在实践中应用机器学习的很大一部分。以下示例在程序 HouseMNIST 中提供。ipynb。 我们从著名的鸢尾花分类问题的例子开始。鸢尾花数据集是在同一天从加拿大魁北克东部加斯佩地区的一个田地收集的。这些数据最早由英国著名统计学家罗纳德·费舍尔在 1936 年的一篇论文中使用。数据由 150 个样本组成，其中 3 种鸢尾花各 50 个样本，称为鸢尾花(0), 鸢尾花 (1) 和鸢尾花 (2)。出于我们的目的，我们通常简单地给每个类一个标签，例如一个数字，如本例中花名后面的括号所示。 该数据集在本书的网页上给出，包含三个名为 iris 的文本文件。数据，特征名称。txt 和 target_names.txt，开始练习数据处理。这些是基本的文本文件，可以通过将它们加载到编辑器中来检查它们的内容。我们现在正在使用程序 iris.ipynb 探索这些数据。数据文件包含特征值和类标签，我们可以使用 NumPy 函数 loadtxt 将这些数据加载到 NumPy 数组中。打印出数组的形状显示有 150 行数据，每个样本 1 行，5 列。前四个值是花的隔膜和踏板的测量长度和宽度。最后一个数字是类标签。以下代码将此数据数组分成特征矩阵和所有样本的目标向量。 ## cs代写|机器学习代写machine learning代考|Image processing and convolutional filters 本节深入探讨一些图像处理概念，并回顾在本书后面变得重要的卷积操作。因此，重要的是要好好复习本节。此外，讨论让我们有机会更多地练习 Python 编程。 我们已经展示了由二维矩阵给出的灰度图像，其中每个分量代表一个像素的灰度级。为了表示彩色图像，我们现在只需要三个通道，每个通道代表一种原色，红色 (R)、绿色 (G) 和蓝色 (B)。这样的 RGB 图像用一个张量表示米×ñ×3， 在哪里米和ñ是水平和垂直分辨率的大小，以像素为单位。读取和显示图像文件包含在 Matplotlib 库中，但也可以使用各种其他包。例如，给定一个测试图像，例如图 2.8B 所示的本书网页上的 motorbike.jpg，将这个图像读入一个数组并绘制它的程序是 形状函数表明该图像的分辨率为600×800具有三个颜色通道的像素。 机器学习的一个主要应用是对象识别，现在我们将举例说明如何使用过滤器来突出图像中的特定特征。假设我们正在寻找照片中某个大小的红点。假设我们得到一张 RGB 图像，如图 2.9A 所示。将该图像读入数组并绘制它的相应程序是 创建一个新的红色像素，产生如图 2.9B 所示的图像。我们将此图像用于以下讨论。 我们想用下面的程序检测的红点是左上角的结构，而不是坐标的红色像素(6,5)我们刚刚在上面手动添加的。我们添加了这个红色像素来讨论如何区分我们正在寻找的主要红色对象和图片中的其他红色对象。分别看红色、绿色和蓝色通道很有趣，如图所示。2.9C. 这些图中的每一个都可以使用代码生成，如下面的红色通道示例所示。 ## cs代写|机器学习代写machine learning代考|Machine learning with sklearn 名为 scikit 的开源系列库建立在 NumPy 和 SciPy 库之上，以提供更多特定领域的支持。本章我们简要介绍 scikit-learn 库，简称 sklearn。该库最初是 David Cournapeau 的 Google Summer of Code 项目，后来发展成为一个开源库，现在提供各种完善的机器学习算法。这些算法以及优秀的文档可在$.

scikit-learn 或简称 sklearn 提供的算法概要和典型工作流程如图 3.1 所示。机器学习方法由此分为分类、回归、聚类和降维。我们稍后将讨论相应算法背后的思想，特别是在本章的后半部分，尽管我们首先将这些方法视为一个黑盒。我们在本章中特别概述了用于分类的典型机器学习设置。在某些应用程序中，无需太多了解这些算法的确切功能即可获得足够的性能，尽管我们稍后将展示将机器学习应用于更具挑战性的情况并避免陷阱需要对算法有更深入的了解。

## 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。