Akbar Formula Calculator

Akbar is a term that can refer to different things depending on the context. However, based on the example you provided, I assume that you are referring to a concept in the field of information retrieval and search engines.

In information retrieval, Akbar's formula is a term frequency-based scoring function that is used to rank documents based on their relevance to a query.
The formula is as follows:

score(D, Q) = Σ w(q) * tf(q, D) * idf(q)

where D represents a document, Q represents a query, w(q) represents a weight assigned to the query term q, tf(q, D) represents the frequency of q in document D, and idf(q) represents the inverse document frequency of q across the entire document collection.

The idea behind the formula is that documents that contain more occurrences of the query terms, and whose query terms are less common in the entire document collection, are more likely to be relevant to the query. The weight w(q) can be used to adjust the importance of different query terms based on their significance to the query.

Here is an example of how the Akbar formula can be used to rank documents for a query:

Suppose we have a document collection that consists of the following three documents:

In addition to......

1. Document 1:
   "The quick brown fox jumps over the lazy dog."
2. Document 2:
   "The dog is a loyal and friendly animal."
3. Document 3:
   "Cats and dogs are popular pets."

Now, let's say we have a query "dog". We can use the Akbar formula to rank the documents based on their relevance to the query as follows:

1. For document 1:
   score(D1, Q) = w("dog") * tf("dog", D1) * idf("dog") = 1 * 1 * log(3/2) = 0.4055
2. For document 2:
   score(D2, Q) = w("dog") * tf("dog", D2) * idf("dog") = 1 * 2 * log(3/2) = 0.8111
3. For document 3:
   score(D3, Q) = w("dog") * tf("dog", D3) * idf("dog") = 1 * 1 * log(3/2) = 0.4055

Therefore, document 2 would be ranked the highest because it has the highest score based on the Akbar formula.

It's worth noting that the Akbar formula is just one of many possible scoring functions that can be used in information retrieval, and different search engines may use different formulas depending on their specific requirements and objectives.