It's similar to how human solve Sudoku. create a hash table (dictionary) val to store possible values in every location. Each time, start from the location with fewest possible values, choose one value from it and then update the board and possible values at other locations. If this update is valid, keep solving (DFS). If this update is invalid (leaving zero possible values at some locations) or this value doesn't lead to the solution, undo the updates and then choose the next value. Since we calculated val at the beginning and start filling the board from the location with fewest possible values, the amount of calculation and thus the runtime can be significantly reduced: The run time is 48-68 ms on LeetCode OJ, which seems to be among the fastest python solutions here. The PossibleVals function may be further simplified/optimized, but it works just fine for now. (it would look less lengthy if we are allowed to use numpy array for the board lol).

It's similar to how human solve Sudoku. create a hash table (dictionary) val to store possible values in every location. Each time, start from the location with fewest possible values, choose one value from it and then update the board and possible values at other locations. If this update is valid, keep solving (DFS). If this update is invalid (leaving zero possible values at some locations) or this value doesn't lead to the solution, undo the updates and then choose the next value. Since we calculated val at the beginning and start filling the board from the location with fewest possible values, the amount of calculation and thus the runtime can be significantly reduced: The run time is 48-68 ms on LeetCode OJ, which seems to be among the fastest python solutions here. The PossibleVals function may be further simplified/optimized, but it works just fine for now. (it would look less lengthy if we are allowed to use numpy array for the board lol).

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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One way to potentially reduce the complexity of computing the hash code for Strings is to compute it only once—when the String is created.Future hashCode calls would return the precomputed value. Because the value of a String never changes, this has the potential to be useful. How would you rate the efficacy of such a method?
Suppose you have the following letter's integer value table: A B C D E F G H I J K L 1 2 3 4 5 6 7 8 9 10 11 12 N O P Q R S T U V W X Y 14 15 16 17 18 19 20 21 22 23 24 25 26 Draw the hash table for each of the following, by using the first 10 distinct letters in your name (you may use your triple name), i.e. if you have similar letters then choose the next one until you have 10 different letters. 1) Use separate chaining of 5 slots, using the hash function h(x) =i% 5 where x represents the letter and i represents the corresponding integer value of that letter from the table above. For example, if you have the letter T in your name, then h(T) = 20% 5 = 0 Use linear probing for a table of 16 slots and hash function, h(x)= i % 16 Use quadratic probing for a table of 16 slots and hash function, h(x)= i % 16. For this step use 6 distinct letters from your name chosen in the same way instead of 10. Aside: Quadratic Probing Quadratic probe sequences cause probes to move away from clusters,…
How to remove all elements of hashmap at once.
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In JAVA, use a HashMap to count and store the frequency counts for all the words from a large text document. Using file util, please. Then, display the contents of this HashMap with words and frequency count. Next, please create a set view of the Map and store the contents in an array. Sort this array based on key value and display it. Finally, sort the array in decreasing order by frequency and display it as well. Please label your explanation in the code Thank you
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