MichaelMaggs - 4 years ago 131

Python Question

I'm trying to learn about Python decorators, and I'd like to understand in more detail exactly how closure applies, for example in this memoization context:

`def memoize(f):`

memo = {}

def helper(x):

if x not in memo:

memo[x] = f(x)

return memo[x]

return helper

@memoize

def fib(n):

if n in (0,1):

return n

return fib(n - 1) + fib(n - 2)

I understand that

`memoize`

`memo`

`helper`

`memoize`

`memo`

`@memoize`

`fib(n)`

`memoize(fib(n))`

Where I am struggling is how the called value of

`n`

`fib(n)`

`x`

`helper(x)`

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Answer Source

You've misunderstood what the `@memoize`

decorator does. The decorator is not called each time you call `fib`

. The decorator is called *just once* per function that is decorated.

The original `fib`

function is *replaced* by the `helper()`

function, with the original function object available as the `f`

closure, together with the `memo`

closure:

```
>>> def fib(n):
... if n in (0,1):
... return n
... return fib(n - 1) + fib(n - 2)
...
>>> fib
<function fib at 0x1023398c0>
>>> memoize(fib)
<function helper at 0x102339758>
```

This gives the replacement `helper`

function *one* closure, and each time you call that `helper()`

function the same `memo`

dictionary is used to look up already-produced results for the current value of `x`

.

You can see this all work in the interpreter:

```
>>> @memoize
... def fib(n):
... if n in (0,1):
... return n
... return fib(n - 1) + fib(n - 2)
...
>>> fib
<function helper at 0x10232ae60>
```

Note the function name there! That's `helper`

. It has a closure:

```
>>> fib.__closure__
(<cell at 0x10232d9f0: function object at 0x10232ade8>, <cell at 0x10232da98: dict object at 0x102353050>)
```

A function object and a dictionary, those are `f`

and `memo`

, respectively. Access the cell contents with `.cell_contents`

:

```
>>> fib.__closure__[0].cell_contents
<function fib at 0x10232ade8>
>>> fib.__closure__[1].cell_contents
{}
```

That's the original decorated function and the empty dictionary alright. Let's use this function:

```
>>> fib(0)
0
>>> fib(1)
1
>>> fib.__closure__[1].cell_contents
{0: 0, 1: 1}
```

That's the resulting values for `x`

set to `0`

and `1`

being stored in the `memo`

dictionary. Next time I call `fib`

with either value, the memo is used. New `x`

values are calculated and added:

```
>>> fib(2)
1
>>> fib.__closure__[1].cell_contents
{0: 0, 1: 1, 2: 1}
```

You can see how well that works by timing how long a larger number takes to calculate:

```
>>> start = time.clock(); fib(500); print(format(time.clock() - start, '.10f'))
139423224561697880139724382870407283950070256587697307264108962948325571622863290691557658876222521294125L
0.0008390000
>>> start = time.clock(); fib(500); print(format(time.clock() - start, '.10f'))
139423224561697880139724382870407283950070256587697307264108962948325571622863290691557658876222521294125L
0.0000220000
>>> start = time.clock(); fib(500); print(format(time.clock() - start, '.10f'))
139423224561697880139724382870407283950070256587697307264108962948325571622863290691557658876222521294125L
0.0000190000
```

After the first call, looking up the memo is 40 times as fast as all the intermediate results have been memoized:

```
>>> len(fib.__closure__[1].cell_contents)
501
```

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