Algorithm for ‘spreading` sum in Hash

may help you . Yes, it's inevitable that there will be collisions when mapping a long message onto a shorter hash, as the hash cannot contain all possible values of the message. For the same reason you cannot 'invert' the hash to uniquely produce either Beowulf or The Hobbit - but if you generated every possible text and filtered out the ones that had your particular hash value, you'd find both texts (amongst billions of others).
The article is saying that it should be hard for an attacker to find or construct a second message that has the same hash value as a first. Cryptographic hash functions are often used as proof that a message hasn't been tampered with - if even a single bit of data flips then the hash value should be completely different.

wish of those help To use Label Spreading you should follow these steps: 1. create a vector of labels (y), where all the unlabeled instances are set to -1. 2. fit the model using your feature data (X) and y. 3. create predict_entropies vector using stats.distributions.entropy(yourmodelname.label_distributions_.T) 4. create an uncertainty index by sorting the predict_entropies vector. 5. send the samples of lowest certainty for label query.
I hope this framework will help you get started.

like below fixes the issue The yellow circle indicates the starting node and the red circle is indicating the goal node. I can't understand why my current node is spreading outwards instead of the image below where the node is just going straight into the goal. , I think you have a problem to get the lowestcost:

private Node getLowestFCost(List<Node> open) {
    int lowestCost = 0;
    int index = 0;
    for (int i = 0; i < open.size(); i++) {
        if (open.get(i).fCost <= lowestCost) {
            lowestCost = open.get(i).fCost;
            index = i;
        }
    }
    return open.get(index);
}

wish help you to fix your issue The solution I wanted is LSH: https://en.wikipedia.org/wiki/Locality-sensitive_hashing
It solves the question, I posed. It's a technique used in Information Retrieval to find duplicates documents or web pages. Thus I can use the same to compare my two strings and get their similarity index.