Smoothing

Numpy kernel smoothing

Numpy kernel smoothing
  1. How does a smoothing kernel work?
  2. What is Gaussian kernel smoothing?
  3. What is smoothing in Python?

How does a smoothing kernel work?

Smoothing with the kernel

The basic process of smoothing is very simple. We proceed through the data point by point. For each data point we generate a new value that is some function of the original value at that point and the surrounding data points.

What is Gaussian kernel smoothing?

The Gaussian smoothing operator is a 2-D convolution operator that is used to `blur' images and remove detail and noise. In this sense it is similar to the mean filter, but it uses a different kernel that represents the shape of a Gaussian (`bell-shaped') hump.

What is smoothing in Python?

Smoothing is a technique that is used to eliminate noise from a dataset. There are many algorithms and methods to accomplish this but all have the same general purpose of 'roughing out the edges' or 'smoothing' some data. There is reason to smooth data if there is little to no small-scale structure in the data.

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