Iru_cache function in Python Programming

The lru_cache function is a memory cache decorator that allows you to store the results of computationally expensive functions. By using this function, we can avoid recalculating the same results for the same arguments. This can be particularly useful if we are working with functions that take a long time to execute.

Let see an example of how we can use the lru_cache function to store the results of a function:

In this example, we have a fibonacci function that calculates the n-th number in the Fibonacci sequence. We have decorated this function with lru_cache to store the results of the function. Now, every time we call the fibonacci function with a given argument, the result is stored in the cache memory. If we call the fibonacci function with the same argument again, the result is returned directly from the cache memory, without having to recalculate the result.

Here is another example that shows how you can use the lru_cache function to store the results of a function that takes multiple arguments:

In this last example, we have an add function that takes two arguments x and y and returns their sum. We have decorated this function with lru_cache to store the results of the function. Now, every time we call the add function with given arguments, the result is stored in the cache memory. If we call the add function with the same arguments again, It will be exactly the same as before, that is to say, the result is returned directly from the cache memory, without having to recalculate the result.

When is it better to use it?

• Repetitive Computations with Identical Inputs: Apply it to functions that are called repeatedly with the same arguments, where each call performs the same computation.
• Performance-Intensive Functions: Use it for functions that are computationally expensive, such as those involving heavy data processing, complex calculations, or deep learning algorithms.
• Limited and Predictable Input Variance: It's most effective when the function has a relatively small and predictable set of input values, making caching more efficient.

By using the lru_cache function, you can significantly improve the performance of your computationally expensive functions. However, it is important to note that the cache memory can consume a lot of memory if not used correctly. So you must be careful when using this function and make sure you use the right cache size for your needs.