Even though two implementations of a hash

table may require the same average number of comparisons, their distributions

might be different. The following experiment will examine this possibility for

linear probing and double hashing. You will need two disjoint lists of names:

one with at least 1000 names and the other with at least 10,000 names.

a. For both of the collision resolution

schemes linear probing and double hashing, calculate the load factor that

results in an average of 1.5 comparisons for an unsuccessful search of a hash

table holding 100 objects. For this load factor, what

Even though two implementations of a hash

table may require the same average number of comparisons, their distributions

might be different. The following experiment will examine this possibility for

linear probing and double hashing. You will need two disjoint lists of names:

one with at least 1000 names and the other with at least 10,000 names.

a. For both of the collision resolution

schemes linear probing and double hashing, calculate the load factor that

results in an average of 1.5 comparisons for an unsuccessful search of a hash

table holding 100 objects. For this load factor, what is the required size of

the hash table?

b. Create two hash tables of the

appropriate size and two corresponding empty lists, which will hold counts. Use

linear probing for one table and double hashing for the other. Inside a loop

that iterates 1000 times, do the following:

Clear the hash tables.

Randomly choose 100 names from the list of

1000 and insert them into the tables.

Randomly choose 100 names from the list of

10,000 and search the tables for each name.

(Each search will be unsuccessful because

the two lists have no names in common.)

Count the number of comparisons made in

each table for the 100 searches and record the count in the list corresponding

to the table. After the iteration is complete, each list should contain 1000

values. Each of these values is the total number of comparisons required to

search for 100 names. Compute and display the average and standard deviation of

each list. We expect the average number of comparisons for both hash tables to

be equal to 150 (1.5 times 100).

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