L2Distanz

L2Distanz, also known as Euclidean distance, is a measure of the straight-line distance between two points in a two-dimensional space. It is calculated using the Pythagorean theorem, which states that the square of the hypotenuse (the straight-line distance) is equal to the sum of the squares of the other two sides. In the context of L2Distanz, these sides are the differences between the coordinates of the two points.

The formula for L2Distanz between two points (x1, y1) and (x2, y2) is:

L2Distanz = sqrt((x2 - x1)^2 + (y2 - y1)^2)

where sqrt denotes the square root.

L2Distanz is widely used in various fields, including mathematics, physics, and computer science. In machine learning, for instance, it is often used as a metric to measure the similarity between data points. It is also used in image processing to compare the similarity between images.

Despite its simplicity, L2Distanz has some limitations. It assumes that all dimensions are equally important, which may not always be the case. It also does not account for the direction of the distance, only the magnitude. For these reasons, other distance metrics, such as Manhattan distance or cosine similarity, may be used in certain applications.