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首页Controlling-Profitability Analysis with SAP

Controlling-Profitability Analysis with SAP

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Controlling-Profitability Analysis with SAP Controlling-Profitability Analysis with SAP

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SAP PRESS is a joint initiative of SAP and Galileo Press. The know-how

offered by SAP specialists combined with the expertise of the Galileo

Press publishing house offers the reader expert books in the field. SAP

PRESS features first-hand information and expert advice, and provides

useful skills for professional decision-making.

SAP PRESS offers a variety of books on technical and business related

topics for the SAP user. For further information, please visit our website:

www.sap-press.com.

Vanda Reis

Actual Costing with the SAP Material Ledger

2012, 504 pp., hardcover

ISBN 978-1-59229-378-0

John Jordan

100 Things You Should Know About Controlling with SAP

2011, 289 pp., hardcover

ISBN 978-1-59229-341-4

John Jordan

Production Variance Analysis in SAP Controlling (2

edition)

2011, 292 pp., hardcover

ISBN 978-1-59229-381-0

Rogerio Faleiros and Alison Kreis Ryan

Customizing Controlling in SAP ERP

2012, ~560 pp., hardcover

ISBN 978-1-59229-401-5

Bonn



Boston

Marco Sisfontes-Monge

Controlling-Profitability Analysis

with SAP

Dear Reader,

Sometimes I like to think of profit as a type of treasure. It can be tricky to find, with

unforeseen obstacles, roadblocks, and even dragons blocking your way! (Okay,

maybe not.) However, I think we can all agree that oftentimes there are obstacles

blocking you from gaining profit, or things that hinder your understanding of the

ways a business can create profit. That’s where this book comes in—think of it as

your treasure map that will enable you to configure your SAP system to find profit

for a business.

It’s been a pleasure to work with Dr. Marco Sisfontes-Monge on the second edition

of this book. I can attest to the fact that he has combed through each chapter to

add more information, explain processes and steps more clearly, and provide you

with the insight you need to understand what Profitability Analysis tools will work

best for your business. He’s also provided an overview of recent changes to

SAP NetWeaver BI tools, as well as an introduction to how the up and coming

SAP HANA will enhance the capabilities of CO-PA. So without hesitation, prepare

to dive into CO-PA with SAP and find the information you need to make your busi-

ness as successful as possible!

We appreciate your business, and welcome your feedback. Your comments and

suggestions are the most useful tools to help us improve our books for you, the

reader. We encourage you to visit our website at www.sap-press.com and share

your feedback about this work.

Thank you for purchasing a book from SAP PRESS!

Laura Korslund

Editor, SAP PRESS

Galileo Press

Boston, MA

laura.korslund@galileo-press.com

www.sap-press.com

Notes on Usage

This e-book is protected by copyright. By purchasing this e-book, you have agreed

to accept and adhere to the copyrights. You are entitled to use this e-book for

personal purposes. You may print and copy it, too, but also only for personal use.

Sharing an electronic or printed copy with others, however, is not permitted, neither

as a whole nor in parts. Of course, making them available on the Internet or in a

company network is illegal as well.

For detailed and legally binding usage conditions, please refer to the section Legal

Notes.

This e-book copy contains a digital watermark, a signature that indicates which

person may use this copy:

Copy No. 4jzq-w672-tv5d-8csy

for personal use of

Mireille Raybaud, user id a5fd3b4f-d601-c76a-0655-736e6618762d

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Continue to refine the following code and don't add any other packages but numpy: def mean_shift2(xs: np.ndarray, num_iter: int = 50, k_type: str = 'rbf', bandwidth: float = 0.1) -> np.ndarray: """ Implement a variant of mean-shift algorithm, with unchanged kernel matrix :param xs: a set of samples with size (N, D), where N is the number of samples, D is the dimension of features :param num_iter: the number of iterations :param k_type: the type of kernels, including 'rbf', 'gate', 'triangle', 'linear' :param bandwidth: the hyperparameter controlling the width of rbf/gate/triangle kernels :return: the estimated means with size (N, D) """ # TODO: change the code below and implement the modified mean-shift return xs

:param bandwidth: the hyperparameter controlling the width of rbf/gate/triangle kernels :return: the estimated means with size (N, D) """ N, D = xs.shape # Initialize the means as the input ...

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![libplctag CI](https://github.com/libplctag/libplctag/workflows/libplctag%20CI/badge.svg?branch=release) libplctag is an open source C library for Linux, Windows and macOS using EtherNet/IP or Modbus TCP to read and write tags in PLCs. The library has been in production since early 2012 and is used by multiple organizations for many tasks including controlling radio telescopes, large and precision manufacturing, controlling fitness equipment, food handling and many, many more. Current Stable Version: 2.5 Old Stable Version: 2.4 ## WARNING - DISCLAIMER Note: PLCs control many kinds of equipment and loss of property, production or even life can happen if mistakes in programming or access are made. Always use caution when accessing or programming PLCs! We make no claims or warrants about the suitability of this code for any purpose. Be careful! ## Get It Do you know what you want already? Download it from the [releases page](https://github.com/libplctag/libplctag/releases)! ## Features ### High Level Features - EtherNet/IP and Modbus TCP support. - Open source licensing under the MPL 2.0 or LGPL 2+. - Pure C library for portability across Linux, Windows and macOS as well as 32-bit and 64-bit. - Support for x86, ARM and MIPS, and probably others. - Very stable API with almost no changes other than feature additions since 2012. - Low memory use and very high performance and capacity. Uses protocol-specific features to increase performance. - Simple API with minimal use of language-specific data to enable easy wrapping in other languages. - Extensive example programs showing use of all library features. - Wrappers for higher level languages like C#/.Net, Julia etc. - Free! ### Detailed Features

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find bug and fix it: def mean_shift2(xs: np.ndarray, num_iter: int = 50, k_type: str = 'rbf', bandwidth: float = 0.1) -> np.ndarray: """ Implement a variant of mean-shift algorithm, with unchanged kernel matrix :param xs: a set of samples with size (N, D), where N is the number of samples, D is the dimension of features :param num_iter: the number of iterations :param k_type: the type of kernels, including 'rbf', 'gate', 'triangle', 'linear' :param bandwidth: the hyperparameter controlling the width of rbf/gate/triangle kernels :return: the estimated means with size (N, D) """ # TODO: change the code below and implement the modified mean-shift kappa = kernel(xs, y=None, k_type=k_type, bandwidth=bandwidth) D = np.diag(kappa.sum(axis=1)) L = D - kappa D_inv_sqrt = np.diag(1 / np.sqrt(np.diag(D))) L_norm = D_inv_sqrt @ L @ D_inv_sqrt for i in range(xs.shape[0]): x = xs[i] for j in range(num_iter): ms = L_norm @ x x = ms / np.linalg.norm(ms) xs[i] = x return xs

:param bandwidth: the hyperparameter controlling the width of rbf/gate/triangle kernels, or 'silverman' for adaptive bandwidth :return: the estimated means with size (N, D) """ N, D = xs.shape ...

Optimize the following code to use the variable: variance in the code. def e_step(xs: np.ndarray, ys: np.ndarray, affine: np.ndarray, translation: np.ndarray, variance: float) -> np.ndarray: """ The e-step of the em algorithm, estimating the responsibility P=[p(y_m | x_n)] based on current model :param xs: a set of points with size (N, D), N is the number of samples, D is the dimension of points :param ys: a set of points with size (M, D), M is the number of samples, D is the dimension of points :param affine: an affine matrix with size (D, D) :param translation: a translation vector with size (1, D) :param variance: a float controlling the variance of each Gaussian component :return: the responsibility matrix P=[p(y_m | x_n)] with size (N, M), which row is the conditional probability of clusters given the n-th sample x_n """ # TODO: Change the code below and implement the E-step of GMM responsibility = np.ones((xs.shape[0], ys.shape[0])) / ys.shape[0] for n in range(xs.shape[0]): for m in range(ys.shape[0]): temp = -0.5 * np.linalg.norm(xs[n] - ys[m] @ affine - translation) ** 2 responsibility[n, m] = 1 / (2 * np.pi) ** (xs.shape[1] / 2) * np.exp(temp) return responsibility / np.sum(responsibility, axis=1, keepdims=True)

:param variance: a float controlling the variance of each Gaussian component :return: the responsibility matrix P=[p(y_m | x_n)] with size (N, M), which row is the conditional probability of ...

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Error = InputPoint- pp->rOut; pp->ContrlOut=0.0407Error-0.04028pp->error_1 //calculate the controlling quantity directly +0.8187pp->u_1+(1-0.8187)pp->u_6; pp->error_2=pp->error_1; pp->error_1=Error; pp->u_6=pp->u_5; /get the next controlling quantity/ pp->u_5=pp->u_4; pp->u_4=pp->u_3; pp->u_3=pp->u_2; pp->u_2=pp->u_1; pp->u_1=pp->ContrlOut; if(pp->ContrlOut>=1.0) /Restricting Maxim PID Out/ pp->ContrlOut=1.0; if(pp->ContrlOut<0) pp->ContrlOut=0; return pp->ContrlOut; //此公式用到的大林算法离散化方法

抱歉，我并没有看到这段代码中使用到大林算法的离散化方法。这段代码是一个PID控制器的实现，其中的公式是PID控制器的控制规律。PID控制器的作用是根据当前的误差（偏差）来计算输出量，以实现对被控对象的控制。...

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