On Monday, April 16, 2018, Raymond Hettinger <raymond.hettin...@gmail.com> wrote:
> > > > On Apr 15, 2018, at 9:04 PM, Peter Norvig <pe...@norvig.com> wrote: > > > > it would be a bit weird and disorienting for the arithmetic operators to > have two different signatures: > > > > <counter> += <counter> > > <counter> -= <counter> > > <counter> *= <scalar> > > <counter> /= <scalar> > > > > Is it weird and disorienting to have: > > > > <str> += <str> > > <str> *= <scalar> > > Yes, there is a precedent that does seem to have worked out well in > practice :-) It isn't exactly parallel because strings aren't containers > of numbers, they don't have & and |, and there isn't a reason to want a / > operation, but it does suggest that signature variation might not be > problematic. > > BTW, do you just want __mul__ and __rmul__? If those went in, presumably > there will be a request to support __imul__ because otherwise c*=3 would > still work but would be inefficient (that was the rationale for adding > inplace variants for all the current arithmetic operators). Likewise, > presumably someone would legitimately want __div__ to support the > normalization use case. Perhaps less likely, there would be also be a > request for __floordiv__ to allow exactly scaled results to stay in the > domain of integers. Which if any of these makes sense to you? > > Also, any thoughts on the cleanest way to express the computation of a > chi-squared statistic (for example, to compare observed first digit > frequencies to the frequencies predicted by Benford's Law)? This isn't an > arbitrary question (it came up when a professor first proposed a variant of > this idea a few years ago). https://en.wikipedia.org/wiki/Chi-squared_distribution https://en.wikipedia.org/wiki/Chi-squared_test https://en.wikipedia.org/wiki/Benford%27s_law (How might one test this with e.g. *double* SHA256?) proportions_chisquare(count, nobs, value=None) https://www.statsmodels.org/dev/generated/statsmodels.stats.proportion.proportions_chisquare.html https://www.statsmodels.org/dev/genindex.html?highlight=chi scipy.stats.chisquare(f_obs, f_exp=None, ddof=0, axis=0) https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.stats.chisquare.html sklearn.feature_selection.chi2(X, y) http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html#sklearn.feature_selection.chi2 kernel_approximation.AdditiveChi2Sampler kernel_approximation.SkewedChi2Sampler http://scikit-learn.org/stable/modules/classes.html#module-sklearn.kernel_approximation has sklearn.metrics.pairwise.chi2_kernel(X, Y=None, gamma=1.0) http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.chi2_kernel.html#sklearn.metrics.pairwise.chi2_kernel sklearn.metrics.pairwise.additive_chi2_kernel(X, Y=None) http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.additive_chi2_kernel.html#sklearn.metrics.pairwise.additive_chi2_kernel ... FreqDist(collections.Counter(odict)) ... sparse-coding ... One-Hot / Binarization http://contrib.scikit-learn.org/categorical-encoding/ StandardScalar (for standardization) refuses to work with sparse matrices: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler > > Raymond > _______________________________________________ > Python-ideas mailing list > Python-ideas@python.org > https://mail.python.org/mailman/listinfo/python-ideas > Code of Conduct: http://python.org/psf/codeofconduct/ >
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