机器学习代写|tensorflow代写|Polynomial modelUsing regression for call-center volume prediction

TensorFlow是一个用于机器学习和人工智能的免费和开源的软件库。它可以用于一系列的任务，但特别关注深度神经网络的训练和推理。

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

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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

机器学习代写|tensorflow代写|Cleaning the data for regression

First, download this data-a set of phone calls from the summer of 2014 from the New York City 311 service-from http://mng.bz/P16w. Kaggle has other 311 datasets, but you’ll use this particular data due to its interesting properties. The calls are formatted as a comma-separated values (CSV) file that has several interesting features, including the following:

• A unique call identifier showing the date when the call was created
” The location and ZIP code of the reported incident or information request
• The specific action that the agent on the call took to resolve the issue
• What borough (such as the Bronx or Queens) the call was made from
• The status of the call
This dataset contains lot of useful information for machine learning, but for purposes of this exercise, you care only about the call-creation date. Create a new file named 311.py. Then write a function to read each line in the CSV file, detect the week number, and sum the call counts by week.

Your code will need to deal with some messiness in this data file. First, you aggregate individual calls, sometimes hundreds in a single day, into a seven-day or weekly bin, as identified by the bucket variable in listing 4.1. The freq (short for frequency) variable holds the value of calls per week and per year. If the $311 \mathrm{CSV}$ contains more than a year’s worth of data (as other 311 CSVs that you can find on Kaggle do), gin up your code to allow for selection by year of calls to train on. The result of the code in listing $4.1$ is a freq dictionary whose values are the number of calls indexed by year and by week number via the period variable. The $t$. tm_year variable holds the parsed year resulting from passing the call-creation-time value (indexed in the CSV as date_idx, an integer defining the column number where the date field is located) and the date_parse format string to Python’s time library’s strptime (or string parse time) function. The date parse format string is a pattern defining the way the date appears as text in the CSV so that Python knows how to convert it to a datetime representation.

机器学习代写|tensorflow代写|What’s in a bell curve? Predicting Gaussian distributions

A bell or normal curve is a common term to describe data that we say fits a normal distribution. The largest $Y$ values of the data occur in the middle or statistically the mean $\mathrm{X}$ value of the distribution of points, and the smaller $Y$ values occur on the early and tail X values of the distribution. We also call this a Gaussian distribution after the famous German mathematician Carl Friedrich Gauss, who was responsible for the Gaussian function that describes the normal distribution.

We can use the NumPy method np.random.normal to generate random points sampled from the normal distribution in Python. The following equation shows the Gaussian function that underlies this distribution:
$$e^{\frac{\left(-(x-\mu)^{2}\right)}{2 \sigma^{2}}}$$
The equation includes the parameters $\mu$ (pronounced $m u$ ) and $\sigma$ (pronounced sigma), where $m u$ is the mean and sigma is the standard deviation of the distribution, respectively. Mu and sigma are the parameters of the model, and as you have seen, TensorFlow will learn the appropriate values for these parameters as part of training a model.

To convince yourself that you can use these parameters to generate bell curves, you can type the code snippet in listing $4.3$ into a file named gaussian.py and then run it to produce the plot that follows it. The code in listing $4.3$ produces the bell curve visualizations shown in figure 4.4. Note that I selected values of mu between $-1$ and 2 . You should see center points of the curve in figure 4.4, as well as standard deviations (sigma) between 1 and 3 , so the width of the curves should correspond to those values inclusively. The code plots 120 linearly-spaced points with $\mathrm{X}$ values between $-3$ and 3 and $\mathrm{Y}$ values between 0 and 1 that fit the normal distribution according to $\mathrm{mu}$ and sigma, and the output should look like figure 4.4.

机器学习代写|tensorflow代写|Training your call prediction regressor

Now you are ready to use TensorFlow to fit your NYC 311 data to this model. It’s probably clear by looking at the curves that they seem to comport naturally with the 311 data, especially if TensorFlow can figure out the values of mu that put the center point of the curve near spring and summer and that have a fairly large call volume, as well as the sigma value that approximates the best standard deviation.

Listing $4.4$ sets up the TensorFlow training session, associated hyperparameters, learning rate, and number of training epochs. I’m using a fairly large step for learning rate so that TensorFlow can appropriately scan the values of mu and sig by taking bigenough steps before settling down. The number of epochs-5,000-gives the algorithm enough training steps to settle on optimal values. In local testing on my laptop, these hyperparameters arrived at strong accuracy $(99 \%)$ and took less than a minute. But I could have chosen other hyperparameters, such as a learning rate of $0.5$, and given the training process more steps (epochs). Part of the fun of machine learning is hyperparameter training, which is more art than science, though techniques such as meta-learning and algorithms such as HyperOpt may ease this process in the future. A full discussion of hyperparameter tuning is beyond the scope of this chapter, but an online search should yields thousands of relevant introductions.

When the hyperparameters are set up, define the placeholders $\mathrm{X}$ and $\mathrm{Y}$, which will be used for the input week number and associated number of calls (normalized), respectively. Earlier, I mentioned normalizing the Y values and creating the ny_train variable in listing $4.2$ to ease learning. The reason is that the model Gaussian function that we are attempting to learn has $\mathrm{Y}$ values only between 0 and 1 due to the exponent e. The model function defines the Gaussian model to learn, with the associated variables mu and sig initialized arbitrarily to 1. The cost function is defined as the L2 norm, and the training uses Gradient descent. After training your regressor for 5,000 epochs, the final steps in listing $4.4$ print the learned values for mu and sig.

机器学习代写|tensorflow代写|Polynomial model

F(X)=在nXn+…+在1X+在0

机器学习代写|tensorflow代写|Application of linear regression

• 马萨诸塞大学阿默斯特分校在 https://scholarworks.umass.edu/data 提供各种类型的小型数据集。
• Kaggle 在 https://www.kaggle.com/datasets 为机器学习竞赛提供所有类型的大规模数据。
= Data.gov (https://catalog.data.gov) 是美国政府的一项开放数据计划，其中包含许多有趣且实用的数据集。

import csv import time
def read(filename, date_idx, date_parse, year, bucket=7)=
days_in_year=365

if t.tm_year == year and吨.tm_yday<(days_in_year-1):

return freq

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。