A Python Decorator similar to but much simpler than functools.lru_cache with the extra ability to select parameters. It can be used to memoize recursive functions.

• Clone this repository and place the lru_cache module in the working directory.

• Import the LRU Cache in your Python code by typing the below command.

  from lru_cache.lru_cache import SelectiveLRUCache

• Decorate a function with the cache by placing the below command just above the function definition. The decorator takes two parameters - maxsize (the maximum size of the LRU Cache) and parameters. The "parameters" parameter is a lambda function which decides which parameters to select. In the below example, the "parameters" lambda selects only the first parameter.

  @SelectiveLRUCache(parameters=lambda x:(x[0],), maxsize=None)
  def fibonacci(n, cntr):

• The "parameters" lambda function takes the list of parameters as input and outputs a tuple of the selected list of parameters. Here the lambda selects the first and third parameters from the list of parameters.

  @SelectiveLRUCache(parameters=lambda x:(x[0], x[2]))

• When tested on the recursive fibonacci function, a 46000 times faster execution and a 29000 times reduction in the number of function calls was achieved.

• Fibonacci(40) without the cache took 331160289 function calls and 92.686 seconds as shown below:

• Fibonacci(40) with the cache took 1140 function calls and only 0.002 seconds to execute as shown below:

• Feel free to open an issue if any bug is found.
• Pull requests are welcome. Just make sure to contribute readable, well commented, and tested code.