> On 2018 Apr 5 , at 12:52 p, Peter O'Connor <peter.ed.ocon...@gmail.com> wrote:
>
> Dear all,
>
> In Python, I often find myself building lists where each element depends on
> the last. This generally means making a for-loop, create an initial list,
> and appending to it in the loop, or creating a generator-function. Both of
> these feel more verbose than necessary.
>
> I was thinking it would be nice to be able to encapsulate this common type of
> operation into a more compact comprehension.
>
> I propose a new "Reduce-Map" comprehension that allows us to write:
> signal = [math.sin(i*0.01) + random.normalvariate(0, 0.1) for i in
> range(1000)]
> smooth_signal = [average = (1-decay)*average + decay*x for x in signal from
> average=0.]
> Instead of:
> def exponential_moving_average(signal: Iterable[float], decay: float,
> initial_value: float=0.):
> average = initial_value
> for xt in signal:
> average = (1-decay)*average + decay*xt
> yield average
>
> signal = [math.sin(i*0.01) + random.normalvariate(0, 0.1) for i in
> range(1000)]
> smooth_signal = list(exponential_moving_average(signal, decay=0.05))
> I've created a complete proposal at:
> https://github.com/petered/peps/blob/master/pep-9999.rst , (and a
> pull-request) and I'd be interested to hear what people think of this idea.
>
> Combined with the new "last" builtin discussed in the proposal, this would
> allow u to replace "reduce" with a more Pythonic comprehension-style syntax.
See itertools.accumulate, comparing the rough implementation in the docs to
your exponential_moving_average function:
signal = [math.sin(i*0.01) + random.normalvariate(0,0.1) for i in
range(1000)]
dev compute_avg(avg, x):
return (1 - decay)*avg + decay * x
smooth_signal = accumulate([initial_average] + signal, compute_avg)
--
Clint
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