Examples of using Euclidean distance in English and their translations into Chinese
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Programming
Euclidean distance is defined as:.
Euclidean distance is estimated as:.
Measurement of the distance uses Euclidean distance.
Euclidean distance is calculated as:.
Measurement of the distance uses Euclidean distance.
How can the euclidean distance be calculated with numpy?
Here the distance is calculated using the Euclidean distance.
We could also use the Euclidean distance to measure similarity.
All Galilean transformations preserve the 3-dimensional Euclidean distance.
In Cartesian coordinates, the Euclidean distance between points p and q is:.
Euclidean Distance can be used to create a simple optical character recognition.
One defined by the Euclidean norm, called the Euclidean distance.
Indicates Euclidean distance, and exp means Euler's number(e) raised to a power.
To calculate that similarity, we will use the Euclidean distance as measurement.
The norm is usually Euclidean distance, although other distance functions are also possible.
It is worth pointing out that the distance r defined above is not limited to the Euclidean distance.
One approach is to limit the euclidean distance to a fixed length, ignoring the final dimension.
Similarity amongst our observations, in the simplest terms,can be stated via Euclidean distance between data points.
The distance is generally the Euclidean distance, but other distances can be used too.
Using euclidean distance works surprisingly well, but of course you can use any kind of classifier of your choice.
A part of this iterative process requires computing the Euclidean distance of each point from each centroid:.
It is usual to use the Euclidean distance, though other distance measures such as the Manhattandistance can be used.
The distance can, in general, be any metric measure:standard Euclidean distance is the most common choice.
In k-means or kNN, we use euclidean distance to calculate the distance between nearest neighbors.
The distance can, in general, be any metric measure:standard Euclidean distance is the most common choice.
Often, that distance function can be Euclidean distance, but other choices include correlation distance and various kinds of angle distances. .
The distance between the point and the neurons is calculated by the Euclidean distance, the neuron with the least distance wins.
So for the set of coordinates in tri from above, the Euclidean distance of each point from the origin(0, 0) would be:.