https://github.com/python/cpython/commit/2f126a70f36e36dd90db53ebdcdff9b990cf3452 commit: 2f126a70f36e36dd90db53ebdcdff9b990cf3452 branch: main author: Raymond Hettinger <[email protected]> committer: rhettinger <[email protected]> date: 2024-01-11T16:21:21-06:00 summary:
Update KDE recipe to match the standard use of the h parameter (gh-#113958) files: M Doc/library/statistics.rst diff --git a/Doc/library/statistics.rst b/Doc/library/statistics.rst index 588c9c0be4ea02..0417b3f38a9807 100644 --- a/Doc/library/statistics.rst +++ b/Doc/library/statistics.rst @@ -1104,17 +1104,15 @@ from a fixed number of discrete samples. The basic idea is to smooth the data using `a kernel function such as a normal distribution, triangular distribution, or uniform distribution <https://en.wikipedia.org/wiki/Kernel_(statistics)#Kernel_functions_in_common_use>`_. -The degree of smoothing is controlled by a single -parameter, ``h``, representing the variance of the kernel function. +The degree of smoothing is controlled by a scaling parameter, ``h``, +which is called the *bandwidth*. .. testcode:: - import math - def kde_normal(sample, h): "Create a continuous probability density function from a sample." - # Smooth the sample with a normal distribution of variance h. - kernel_h = NormalDist(0.0, math.sqrt(h)).pdf + # Smooth the sample with a normal distribution kernel scaled by h. + kernel_h = NormalDist(0.0, h).pdf n = len(sample) def pdf(x): return sum(kernel_h(x - x_i) for x_i in sample) / n @@ -1128,7 +1126,7 @@ a probability density function estimated from a small sample: .. doctest:: >>> sample = [-2.1, -1.3, -0.4, 1.9, 5.1, 6.2] - >>> f_hat = kde_normal(sample, h=2.25) + >>> f_hat = kde_normal(sample, h=1.5) >>> xarr = [i/100 for i in range(-750, 1100)] >>> yarr = [f_hat(x) for x in xarr] _______________________________________________ Python-checkins mailing list -- [email protected] To unsubscribe send an email to [email protected] https://mail.python.org/mailman3/lists/python-checkins.python.org/ Member address: [email protected]
