[Python-ideas] FW: Inline Functions - idea

[Alex Rodrigues]

I have to agree about matrix_multiply, it's not that clean. But try writing it out without inline and you'll find that it is quite a bit longer. If we were aiming for more readability I suppose we could do this

def matrix_multiply(matrix, matrix2):
    def dot_product(i, j):
        sum = 0
        for x in range(len(matrix2)):
            sum += matrix[i][x] * matrix2[x][j]
        return sum

result = copy.copy(matrix)
element_wise(result[i][j] = dot_product(i, j))
return result

Which is still superior to the best syntax currently available. So being concerned about the readablility of the function, while legitimate, is not actually a knock on the syntax just on the way I wrote it.

> ________________________________
>> From: amber.yust at gmail.com
>> Date: Mon, 10 Feb 2014 19:20:41 +0000
>> Subject: Re: [Python-ideas] Inline Functions - idea
>> To: lemiant at hotmail.com; python-ideas at python.org
>>
>> I find your example code far less readable than just writing out the
>> loops in the appropriate functions, especially for matrix_multiply.
>>
>> On Mon Feb 10 2014 at 10:51:35 AM, Alex Rodrigues
>> <lemiant at hotmail.com<mailto:lemiant at hotmail.com>> wrote:
>> Over the last few days I've been considering a lot of what we talked
>> about regarding Python scoping and inline functions. Today I found a
>> further use case for inline functions (one which is a fair bit harder
>> to replicate using standard techniques). I was working on writing a
>> basic linear algebra calculator for school and one of the annoying
>> things about doing it is that every time you want to do an element-wise
>> operation on the matrix (which is a list of lists) you have to nest
>> what is essentially one line of code inside a pair of for loops.
>> Below I've done three basic linear algebra functions which are written
>> as if there was a builtin "inline()" which transformed a standard
>> function into an inline function and also assuming that python had more
>> powerful lambdas. I feel that these represent a good use case, because
>> the inline function serves to make the code both shorter and clearer.
>>
>>
>> import copy
>>
>> @inline
>> def element_wise(func):
>> func = inline(func)
>> for i, row in enumerate(matrix):
>> result.append([0]*len(row))
>> for j, el in enumerate(row):
>> func()
>>
>> def is_symmetric(matrix):
>> element_wise(lambda: if matrix[i][j] != matrix[j][i]: return False)
>> return True
>>
>> def matrix_add(matrix, matrix2):
>> result = copy.deepcopy(matrix)
>> element_wise(lambda: result[i][j] = matrix[i][j] + matrix2[i][j])
>> return result
>>
>> def matrix_multiply(matrix, matrix2):
>> result = copy.deepcopy(matrix)
>> element_wise(lambda: result[i][j] = sum([a*b for a,b in
>> zip(matrix[i], [r[j] for r in matrix2])]))
>> return result
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