semvec/pypi-packages huggingface dataset:
bloomf 0.3
Simple Bloom Filter implmentation in Python

# Bloom Filter

Implemented in Python 3.

- The price we pay for efficiency through bloom filters is that it is probabilistic in nature that means, there might be some **_False Positive_** results. False positive means, it might tell that given username is already taken but actually itâ€™s not.
- Not being **_False Negative_** such that telling that username doesn't exist while it is there, i.e., if exists it reports it's existenece in terms of maybe, else if not present it is 100% confident to report the same.
- Deleting elements from filter is not possible because, if we delete a single element by clearing bits at indices generated by k hash functions, it might cause deletion of few other elements.

## Installation

**`pip install bloomf==0.2`**

Distributed as a [PyPi](https://pypi.org/project/bloomf/) Package.

## Usage

You can use this bloom filter as follows -

```python
from bloomf import BloomFilter

n = 10  # number of items to be added
p = 0.04  # FP Probablity

filter = BloomFilter(n, p)

print("Size of bit array: {}" . format(filter.size))
print("False positive Probability: {}" . format(filter.fp_prob))
print("Number of hash functions: {}" . format(filter.hash_count))

word_present = ['abound', 'abounds', 'abundance', 'abundant', 'accessable', 'bloom', 'blossom', 'bolster', 'bonny', 'bonus', 'bonuses']
word_absent = ['bluff', 'cheater', 'hate', 'war', 'humanity', 'racism', 'hurt', 'facebook', 'sambhav', 'twitter']

for i in word_present:
    filter.add(i)

test_words = word_present[:5] + word_absent

for word in test_words:
    if filter.check(word):
        if word in word_absent:
            print("'{}' is a false positive!" . format(word))
        else:
            print("'{}' is a probably present!" . format(word))
    else:
        print("'{}' is 100% not present!" . format(word))
```

### Dependencies

- bitarray
- mmh3