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In mathematics it can be quite tedious to re-calculate values every time for a non-changing value or list. Thus look-up tables are handy to have to save time on re-calculating values. This can also be useful in computer science to save time on getting a stored result in a database vs. recalculating the the result.

In mathematics it can be quite tedious to re-calculate values every time for a non-changing value or list. Thus look-up tables are handy to have to save time on re-calculating values. This can also be useful in computer science to save time on getting a stored result in a database vs. recalculating the the result.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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In mathematics it can be quite tedious to re-calculate values every time for a non-changing value or list. Thus look-up tables are handy to have to save time on re-calculating values. This can also be useful in computer science to save time on getting a stored result in a database vs. recalculating the the result. Define a function lookup_table() that takes in 2 parameters: operations is a dictionary that has function names as its keys and function objects as its values • input_value (list, float, or integer) is the input value on which you want to run all the defined functions in the operations dictionary. The function will return a dictionary that contains the key names from the operations parameter dictionary and the values will be results obtained by applying functions stored in operations dictionary values to the given input_value. For example: a = {'sum': sum, 'avg’: np.median} result = lookup_table(a, [2, 4, 6]) . The result would be {'sum': 12, 'avg’: 4.0} Your solution should not import numpy. Hint do not over think this question, or be held up by how functions can be passed into a function and then called again. Its as simple as: result = operations ['sum'](input_value)
Transcribed Image Text:In mathematics it can be quite tedious to re-calculate values every time for a non-changing value or list. Thus look-up tables are handy to have to save time on re-calculating values. This can also be useful in computer science to save time on getting a stored result in a database vs. recalculating the the result. Define a function lookup_table() that takes in 2 parameters: operations is a dictionary that has function names as its keys and function objects as its values • input_value (list, float, or integer) is the input value on which you want to run all the defined functions in the operations dictionary. The function will return a dictionary that contains the key names from the operations parameter dictionary and the values will be results obtained by applying functions stored in operations dictionary values to the given input_value. For example: a = {'sum': sum, 'avg’: np.median} result = lookup_table(a, [2, 4, 6]) . The result would be {'sum': 12, 'avg’: 4.0} Your solution should not import numpy. Hint do not over think this question, or be held up by how functions can be passed into a function and then called again. Its as simple as: result = operations ['sum'](input_value)
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