Read e-book online Data Structures Succinctly Part 2 PDF

By Robert Horvick

Information constructions Succinctly half 2 is your concise consultant to bypass lists, hash tables, tons, precedence queues, AVL bushes, and B-trees. As with the 1st booklet, youll find out how the buildings behave, how you can have interaction with them, and their functionality barriers. beginning with pass lists and hash tables, after which relocating to advanced AVL bushes and B-trees, writer Robert Horvick explains what each one buildings tools and periods are, the algorithms in the back of them, and what's essential to retain them legitimate. The booklet additionally positive factors downloadable code samples and bright diagrams that will help you visualize the extra summary options, like node top and node rotations.

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If there are not enough characters, 0 is used. Length) { return (int)str[index]; } return 0; } The last two hashing functions are conceptually simple and also simple to implement. But how good are they? I created a simple test that generated one million unique values by converting GUIDs to strings. I then hashed those one million unique strings and recorded the number of hash collisions, which occur when two distinct values have the same hash value. 75495% As you can see, both hash algorithms distributed the hash values relatively evenly with DJB2 having slightly better distribution than the folding hash.

The string "Robert Horvick" will always return the same hash code (14). But this method does not have uniform distribution. What would happen if we had one million unique strings, each of which was 50 characters long? Each of them would have a hash code of 50. This is not a uniform distribution, and therefore not a suitable hash algorithm for strings. Here’s a slightly better (bad) example: private int AdditiveHash(string input) { int currentHashValue = 0; foreach (char c in input) { unchecked { currentHashValue += (int)c; } } return currentHashValue; } This hash function has only slightly better uniformity than the length-based hash.

The string "Robert Horvick" will always return the same hash code (14). But this method does not have uniform distribution. What would happen if we had one million unique strings, each of which was 50 characters long? Each of them would have a hash code of 50. This is not a uniform distribution, and therefore not a suitable hash algorithm for strings. Here’s a slightly better (bad) example: private int AdditiveHash(string input) { int currentHashValue = 0; foreach (char c in input) { unchecked { currentHashValue += (int)c; } } return currentHashValue; } This hash function has only slightly better uniformity than the length-based hash.

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Data Structures Succinctly Part 2 by Robert Horvick


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