How to use bloom filters for probabilistic indexing and membership testing in Go?

To use bloom filters for probabilistic indexing and membership testing in Go, follow these steps:

1. Install the bloom filter package from GitHub by executing the following command in your terminal:

   go get github.com/wangjia184/sortedset

2. Import the required packages in your Go file:

   import ( "github.com/wangjia184/sortedset" )

3. Create a new bloom filter by specifying the desired capacity and false positive rate:

   bf := sortedset.NewBloomFilter(capacity, falsePositiveRate)

4. Add elements to the bloom filter using the Add method:

   bf.Add(element)

5. Check the membership of an element using the Contains method:

   if bf.Contains(element) { // Element is likely to be present in the set } else { // Element is definitely not present in the set }

6. Additionally, you can calculate the count of elements in the bloom filter using the Count method:

   count := bf.Count()

7. Finally, to clear the bloom filter and remove all elements, use the Clear method:

   bf.Clear()

Remember that bloom filters have a probabilistic nature. The false positive rate determines the accuracy of membership testing, with lower values leading to fewer false positives but requiring more memory.

Ensure that you adjust the capacity and the false positive rate according to your specific use case to achieve the desired level of accuracy and efficiency.