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In this approach, we can utilize a dictionary (or map) to cache the results of the function calls. The keys in this dictionary will be the string representation of the input arguments, and the values will be the corresponding function output. We will also maintain a count of how many unique calls have been made with new inputs.
The time complexity for each call is O(1) due to dictionary lookups. The space complexity is O(n) in the worst case, where n is the number of unique calls made, due to storage in the cache.
1function memoize(fn) {
The memoize function accepts a function and returns a memoized version of it. The cache is an object and each set of arguments is serialized as a string key. The function updates and checks the cache before invoking the original function. There is also a static method to retrieve the call count.
Preserving the order and distinction of each argument set is crucial and can be achieved using tuples for arguments. With languages like Python, tuples can directly represent immutable and hashable keys for our cache. Similar adaptations exist for other languages, respecting each language's constraints around tuple-like structures and hashable types.
O(1) lookup time in the cache; Space complexity is O(n), dependent on the number of distinct calls stored.
1class Memoize:
2 def __init__(self, func):
3 self.func = func
4 self.cache = {}
5 self.call_count = 0
6
7 def __call__(self, *args):
8 key = tuple(args)
9 if key in self.cache:
10 return self.cache[key]
11 else:
12 result = self.func(*args)
13 self.cache[key] = result
14 self.call_count += 1
15 return result
16
17 def getCallCount(self):
18 return self.call_countThis approach defines the key for cache lookups as a tuple of the input arguments, leveraging Python's native tuple hashability to allow efficient cache operations.