## 数学代写|matlab代写|STA518

2022年12月27日

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

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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
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• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础
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## 数学代写|matlab代写|Generating a Movie Database

We first need to come up with a method for characterizing movies. Table $4.1$ gives our system. MPAA stands for Motion Picture Association of America. It is an organization that rates movies. Other systems are possible, but this will be sufficient to test out our deep learning system. Three of the data points that will be used are strings and two are numbers. One number, length, is a continuum, while ratings have discrete values. The second number, quality, is based on the “stars” in the rating. Some movie databases, like IMDB, have fractional values because they average over all their users. We created our own MPAA ratings and genres based on our opinions. The real MPAA ratings may be different.

Length can be any duration. We’ll use randn to generate the lengths around a mean of $1.8$ hours and a standard deviation of $0.15$ hours. Length is a floating-point number. Stars are one to five and must be integers.

We created an Excel file with the names of 100 real movies, which is included with the book’s software. We assigned random genres and MPAA ratings (PG, R, and so forth) to them. We then saved the Excel file as tab-delimited text and search for tabs in each line. (There are other ways to import data from Excel and text files in MATLAB; this is just one example.) We then assign the data to the fields. The function will check to see if the maximum length or rating is zero, which it is for all the movies in this case, and then create random values. You can create a spreadsheet with rating values as an extension of this recipe. We use str2double since it is faster than str2num when you know that the value is a single number. fgetl reads in one line and ignores the end of line characters.
You’ll notice that we check for NaN in the length and rating fields since

## 数学代写|matlab代写|Generating a Viewer Database

Each watcher will have seen a fraction of the 100 movies in our database. This will be a random integer between 20 and 60 . Each movie watcher will have a probability for each characteristic: the probability that they would watch a movie rated one or five stars, the probability that they would watch a movie in a given genre, etc. (Some viewers enjoy watching so-called “turkeys”!) We will combine the probabilities to determine the movies the viewer has watched. For mPAA, genre, and rating, the probabilities will be discrete. For the length, it will be a continuous distribution. You could argue that a watcher would always want the highest-rated movie, but remember this rating is based on an aggregate of other people’s opinions and so may not directly map onto the particular viewer. The only output of this function is a list of movie numbers for each user. The list is in a cell array.

We start by creating cell arrays of the categories. We then loop through the viewers and compute probabilities for each movie characteristic. We then loop through the movies and compute the combined probabbilities. This results in a list of movies watched by each viewèr.

We use bar charts throughout. Notice how we make the $x$ labels strings for the genre and so on. We also rotate them 90 degrees for clarity. The length is the number of movies longer than the number on the $x$-axis.

This data is based on our viewer model from a recipe in Section $4.3$ which is based on joint probabilities. We will train the neural net on a subset of the movies. This is a classification problem. We just want to know if a given movie would be picked or not picked by the viewer.
We use patternnet to predict the movies watched. This is shown in the next code block. The input to patternnet is the sizes of the hidden layers, in this case, a single layer of size 40. We convert everything into integers. Note that you need to round the results since patternnet does not return integers, despite the label being an integer. patternnet has methods train and view.

# matlab代写

## 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。