Step by Step Guide to Memoization in Java for Machine Learning

Memoization is a caching and optimization technique for simplifying a complex function that is slow to run every time and provides the same output and takes input as an argument. To speed up this function, we can cache the results of running the function so that when the function is run with the same inputs, we can use the cached value instead of recalculating the value. It is really useful in programming but it is most useful is in dynamic programming like machine learning. In this blog post, we at Oodles, as a Machine Learning Development Company, provide a comprehensive guide to implementing memoization in Java for machine learning. 

The concept of Memoization can be also used in AI. The automation of memoization is used as a software engineering tool for AI systems. The package we use for AI is CLAMP (Common Lisp Automatic Memoization Package).

Implementation of Memoization in Java for Machine Learning

Here is the basic example of Memoization code in JavaScript:-

function square(n) {
  let total = 0;
  for (let i=1;i<=n;i++){
    for (let k=1;k<=n;k++){
    total += 1;
       }
     }
  return total;
}

console.log(square(5000));
console.log(square(5000));
console.log(square(5000));

This is a normal square function where we pass a number in the place of (n) and it will give the output of it as shown in the console.log() value.

If we console.log() the same bigger value multiple times our output of the program will be slow to overcome this we use memoization.

We will store the output in a variable so that when we provide the same input values multiple times it will return the other ones instantaneously. 

In this scenario, the output will be shown faster than the previous code.

const previousValue=[];

function square(n) {
  if(previousValue[n]!= null){
    return previousValue[n]
}
  let total = 0;
  for (let i=1;i<=n;i++){
    for (let k=1;k<=n;k++){
    total += 1;
       }
     }
  previousValue[n] = total;
  return total;
}

console.log(square(5000));
console.log(square(5000));
console.log(square(5000));

Now when we print the total it will take time for the first one to display but for the others, it will fetch the values from the variable (previous value) which is stored in an array.

Memoization in AI and Machine Learning

Memoization can be used in multiple programming languages. We can use memoization in Artificial Intelligence. In AI, we use it as automatic memoization, though it is not necessary. The Alternative of automatic memorization can be Handcrafted memoization, Dynamic code, and development of new algorithms. Memoization in AI also opens new opportunities for deploying predictive analytics services to build consumer-oriented applications and solutions.

Memoization is appropriate for AI applications. Automatic memoization allows the developers to write functions without a look at efficiency, though being assured of good performance. By allowing the developer to avoid these efficiency concerns automatic memoization ease the kind of programming used in the development of most AI systems.

Uses of Memoization

1. We can use it while fetching the result from an API using the fetch API method. Memoization will allow us to save making calls again to the server when we already know the output.

2. We can use it in Dynamic programming. In dynamic programming, we call it in a recursive function that calls itself multiple times with the same input we can memoize those inputs.

3. It provides Quality of the solution because it is already been written and debugged.

Conclusion

Memoization is a great benefit but It takes heavy memory usage for speed. We might not notice it in low memory functions but it would have an impact when dealing with RAM-concentrated bigger functions. Automatic memoization is a powerful tool that allows many simple but inefficient algorithms to be useful in programming.