[issue28956] return minimum of modes for a multimodal distribution instead of raising a StatisticsError

[Srikanth Anantharam]

Srikanth Anantharam added the comment:

@steven:

data = [1, 2, 3, 4, 4, 4, 5, 6, 7, 7, 8, 8, 8, 8, 8, 8, 8, 9, 9]
is clearly unimodal with mode 8

data would have been bimodal if 4 repeated exactly the same (7) number of
times as 8, like this:
data = [1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 5, 6, 7, 7, 8, 8, 8, 8, 8, 8, 8, 9, 9]

in which case the new patch in PR 50 would return a tuple
(4, 8)

Thanks & Regards
Srikanth Anantharam
+91 7204 350429
https://sria91.github.io/

Sent from Android

On 13-Dec-2016 3:24 PM, "Steven D'Aprano" <report at bugs.python.org> wrote:

Steven D'Aprano added the comment:

On Tue, Dec 13, 2016 at 09:35:22AM +0000, Srikanth Anantharam wrote:
>
> Srikanth Anantharam added the comment:
>
> A better choice would be to return a tuple of values (sliced from the
> table). And let the user decide which one to use.

The current mode() function is designed for a very basic use-case, where
you have an obvious single mode from discrete data.

The problem with dealing with multiple modes is that its not easy to
tell the difference between a genuinely multi-modal sample and one which
just happens to have a few samples with the same value:

data = [1, 2, 3, 4, 4, 4, 5, 6, 7, 7, 8, 8, 8, 8, 8, 8, 8, 9, 9]

Assuming the sampling is fair, 8 is clearly the mode; but is it bimodal
with 4 the second mode? Or perhaps even four modes, 8, 4, 7 and 9?

I have plans for introducing a binning function to collect data into
bins and run statistics on the bins. That might be a better way to deal
with multi-modal samples: if you bin the data (for discrete data, use a
bin size of 1) and then look at the frequencies, you can decide how many
modes there are.

Thanks for the suggestion.

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Python tracker <report at bugs.python.org>
<http://bugs.python.org/issue28956>
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_______________________________________
Python tracker <report at bugs.python.org>
<http://bugs.python.org/issue28956>
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