[{"id":981,"title":"Average difference entropy","sha":"98f1536ea2cca26dac907b958904b7a5","user_id":null,"state":"published","body":"Calculating Shannon entropy on octet streams can be confusing, especially with those who lack a background in mathematics. It's also not necessary in many cases. A more intuitive calculation, which is also easier to implement, involves calculating the average difference.\r\n\r\nAn informal description of the algorithm:\r\n\r\n- Find the absolute difference of each octet, as a numeric value, against its successor. Pad with 0 values where the calculation is impossible.\r\n- Exclude the left-most and right-most values.\r\n- Take the average of those differences.\r\n\r\nAn example implementation in Python:\r\n\r\n    def entropy(data, encoding=None):\r\n        if not isinstance(data, bytes):\r\n            data = bytes(data, encoding=encoding)\r\n        data = list(data)\r\n        A = [0] + data\r\n        B = data + [0]\r\n        diff = [abs(B[i] - A[i]) for i in range(len(data))][1:-1]\r\n        return sum(diff) / len(diff)\r\n\r\n","created_at":"2022-06-13T00:55:54.431Z","updated_at":"2022-06-15T06:00:38.223Z","zenodo_id":6644371,"doi":"https://doi.org/10.5281/zenodo.6644371","subject":null,"tags":["entropy","computer science","strings"],"vote_count":0,"view_count":1,"score":0.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null}]