Table of Contents

Enum SamplingType

Namespace
AiDotNet.Enums
Assembly
AiDotNet.dll

Specifies the method used to sample or combine values when reducing data dimensions.

public enum SamplingType

Fields

Average = 1

Takes the average (mean) value from the input region.

For Beginners: Average sampling calculates the mean of all values in a group.

For example, if you have these numbers: [2, 5, 1, 3], Average sampling would give you 2.75.

This is useful for:

  • Smoothing out noise in the data
  • Capturing the general trend of all values in the region
  • Reducing the impact of outliers or extreme values

Think of it like measuring the average temperature across a city instead of just the hottest spot. It gives you a more balanced representation of the entire region.

L2Norm = 2

Calculates the L2 norm (Euclidean norm) of the values in the input region.

For Beginners: L2Norm sampling uses a special mathematical formula to combine values.

It works by:

  1. Squaring each number
  2. Adding up all the squared values
  3. Taking the square root of the sum

For example, if you have these numbers: [2, 5, 1, 3], L2Norm sampling would give you: v(2² + 5² + 1² + 3²) = v(4 + 25 + 1 + 9) = v39 ˜ 6.24

This is useful for:

  • Measuring the overall "energy" or "strength" of a signal
  • Giving more weight to larger values without ignoring smaller ones
  • Certain specialized neural network architectures

Think of it like measuring how "impactful" a group of values is collectively, with larger values having more influence than smaller ones.

Max = 0

Takes the maximum value from the input region.

For Beginners: Max sampling simply picks the largest number from a group of values.

For example, if you have these numbers: [2, 5, 1, 3], Max sampling would give you 5.

This is commonly used in neural networks for:

  • Detecting if a feature is present anywhere in the region
  • Reducing the size of images while preserving important details
  • Making the model less sensitive to the exact position of features

Think of it like looking at a group of mountains and recording only the height of the tallest one. It's good at preserving strong signals and ignoring weaker ones.

Remarks

For Beginners: Sampling is how we summarize a group of numbers into a single value.

In AI, we often need to take a collection of values (like a grid of pixels in an image) and represent them with fewer values. This process is called "downsampling" or "pooling".

Think of it like summarizing a neighborhood on a map:

  • You could pick the tallest building (Max)
  • You could calculate the average building height (Average)
  • You could use a special mathematical formula (L2Norm)

Different sampling types give different results and are useful in different situations.

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