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content/blog/pymemoization.md

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+---
+title: "Quick Python: Memoization"
+date: 2020-03-30T17:31:55-04:00
+draft: false
+tags: ["python"]
+---
+
+There is often a trade-off when it comes to efficiency of CPU vs memory usage. In this post, I will show how the [`lru_cache`](https://docs.python.org/3/library/functools.html#functools.lru_cache) decorator can cache results of a function call for quicker future lookup.
+
+```python
+@lru_cache(maxsize=2**7)
+def fib(n):
+    if n == 1:
+        return 0
+    if n == 2:
+        return 1
+    return f(n - 1) + f(n - 2)
+```
+
+In the code above, `maxsize` indicates the number of calls to store. Setting it to `None` will make it so that there is no upper bound. The documentation recommends setting it equal to a power of two.
+
+Do note though that `lru_cache` does not make the execution of the lines in the function faster. It only stores the results of the function in a dictionary.