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This approach utilizes a hash (or map) to store computed results of the function, where the keys are generated based on the input parameters. By serializing the inputs into a unique key (e.g., using JSON), we can easily check whether a computation for the same inputs has been already done and retrieve the result from the cache if it has.
The time complexity of this solution is O(1) on average for each call due to hash map lookups. The space complexity is O(n) where n is the number of unique input sets to the function.
1function memoize(fn) {
2This JavaScript implementation uses JSON.stringify to create a unique key for different sets of arguments. It leverages a Map object to store the computed results associated with each key. This allows for efficient retrieval of cached results.
This approach is similar to hash-based memoization but emphasizes generating a custom key for complex input types that might not be directly hashable. This is particularly useful for composite objects or nested data structures where default serialization (as in JSON) is inadequate or inefficient.
The time complexity is O(1) on average for cache operations, assuming good hashCode implementations for objects. Space complexity is O(n) depending on unique input sets.
1#include <unordered_map>
2#include <tuple>
3#include <iostream>
4#include <functional>
5
6template<typename Ret, typename... Args>
7auto memoize(std::function<Ret(Args...)> fn) {
8 std::unordered_map<std::tuple<Args...>, Ret> cache;
9 return [fn, cache](Args... args) mutable -> Ret {
10 auto key = std::make_tuple(args...);
11 if (cache.find(key) == cache.end()) {
12 cache[key] = fn(args...);
13 }
14 return cache[key];
15 };
16}The C++ solution creates a tuple from the function arguments to form a key for an unordered map. This key is then used to store the function's results, ensuring that repeated calls with the same parameters do not recompute the result.