Search results
Results from the WOW.Com Content Network
The Python hash is still a valid hash function when used within a single run, but if the values are persisted (for example, written to disk), they can no longer be treated as valid hash values, since in the next run the random value might differ.
A keyed hash function based on Keccak. Can also be used without a key as a regular hash function. KMAC256(K, X, L, S) KMACXOF128(K, X, L, S) KMACXOF256(K, X, L, S) TupleHash128(X, L, S) A function for hashing tuples of strings. The output of this function depends on both the contents and the sequence of input strings. TupleHash256(X, L, S)
The hash function in Java, used by HashMap and HashSet, is provided by the Object.hashCode() method. Since every class in Java inherits from Object, every object has a hash function. A class can override the default implementation of hashCode() to provide a custom hash function more in accordance with the properties of the object.
In Python, functions are first-class objects that can be created and passed around dynamically. Python's limited support for anonymous functions is the lambda construct. An example is the anonymous function which squares its input, called with the argument of 5:
Here the index can be computed as some range of bits of the hash function. On the other hand, some hashing algorithms prefer to have the size be a prime number. [19] For open addressing schemes, the hash function should also avoid clustering, the mapping of two or more keys to consecutive slots. Such clustering may cause the lookup cost to ...
The most frequently used general-purpose implementation of an associative array is with a hash table: an array combined with a hash function that separates each key into a separate "bucket" of the array. The basic idea behind a hash table is that accessing an element of an array via its index is a simple, constant-time operation.
The hash join is an example of a join algorithm and is used in the implementation of a relational database management system.All variants of hash join algorithms involve building hash tables from the tuples of one or both of the joined relations, and subsequently probing those tables so that only tuples with the same hash code need to be compared for equality in equijoins.
Instead of maintaining a dictionary, a feature vectorizer that uses the hashing trick can build a vector of a pre-defined length by applying a hash function h to the features (e.g., words), then using the hash values directly as feature indices and updating the resulting vector at those indices. Here, we assume that feature actually means ...