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Function Point Counting Practices Manual 4.3

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Function Point Counting

Practices Manual

Release 4.3

International Function Point

Users Group (IFPUG)

Function Point Counting Practices Manual

Release 4.3

Chairperson, Counting Practices Committee

Adri Timp

Equens SE, Netherlands

cpc@ifpug.org

reproduce portions of this document within their internal counting practices manuals. If portions of this document

are used, the following text must appear on the title page of the derivative document: "This document contains

material that has been extracted from the IFPUG Counting Practices Manual. It is reproduced in this document with

permission of IFPUG."

ISBN 978-0-9753783-3-5

Release 4.3, January 2010

This release replaces Release 4.2.1, which is now obsolete.

Changes are made periodically to the information within.

Counting Practices Committee

Bonnie S. Brown, EDS an HP Company

Royce Edwards, Software Composition Technologies

E. Jay Fischer, JRF Consulting, Inc.

David Garmus, The David Consulting Group

Janet Russac, Software Measurement Expertise, Inc.

Adri Timp, Equens SE, Netherlands

Peter Thomas, Steria

For information about additional copies of this manual, contact

IFPUG

191 Clarksville Road

Princeton Junction, NJ 08550

U.S.A.

(609) 799-4900

E-mail: ifpug@ifpug.org

Web:

http://www.ifpug.org

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IPFUG官方功能点识别指南-最新版4.3（英文） Function Point Counting Practices Manual 4.3

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yolov5 counting

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collect(Collectors.counting())

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Collectors.counting是一个用于统计流中元素个数的终止操作。它返回的是一个Long类型的结果，表示流中的元素数量。在你提供的代码示例中，通过使用list.stream().collect(Collectors.counting())，可以统计列表中的...

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lake counting python解法

Lake counting问题是指给定一个二维矩阵，其中字符‘W’代表水，字符‘.’代表陆地，求出矩阵中连通的水的区域个数。下面是Python解法的示例代码： ```python def dfs(grid, i, j): if i < 0 or j < 0 or i >= len...

Imagine that we want to determine whether unemployment was high (> 6.5), medium (4.5 < x <= 6.5), or low (<= 4.5) for each state and each month. Write a Python function that takes a single number as an input and outputs a single string noting if that number is high, medium, or low. Pass your function to applymap (quiz: why applymap and not agg or apply?) and save the result in a new DataFrame called unemp_bins. (Challenging) This exercise has multiple parts: Use another transform on unemp_bins to count how many times each state had each of the three classifications. - Hint 1: Will this value counting function be a Series or scalar transform? - Hint 2: Try googling "pandas count unique value" or something similar to find the right transform. Construct a horizontal bar chart of the number of occurrences of each level with one bar per state and classification (21 total bars). (Challenging) Repeat the previous step, but count how many states had each classification in each month. Which month had the most states with high unemployment? What about medium and low?

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