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### What is the issue?
I am using the ollama Python library for all the results I am getting.
As I create embeddings using ollama.embed() I get progressively worse embeddings as the batches are …
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Ref numpy
a = np.array([1,2,3,2,3,4,3,4,5,6])
b = np.array([7,2,10,2,7,4,9,4,9,8])
1.distance=np.sqrt(np.sum((a-b)**2))
2.distance=np.linalg.norm(a - b)
It is hoped that GPU can accelerate the calcul…
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#### Description
Precomputed distances are treated as "unsupported" by AgglomerativeClustering, regardless of the provenance. (e.g. sklearn.metrics.euclidean_distances)
This restriction makes it…
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Hello Desislava,
Would there be a way to feed EEMS a pre-defined distance matrix rather than have the program calculate it itself? We are using the program for marine plants dispersed by sea curren…
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Currently, large graph spaces are nowhere near as fast as continuous spaces.
Examples:
## Distance between random points
- Euclidean 2D: 4.88 µs
- 10 x 10 grid graph: 4.7 µs
- Manhattan Stree…
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Hey,
thank you for this very convenient package.
I am digging around trying to understand how it works but can't find how are the distances
computed.
What are the metrics used to compute ecol…
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Hello,
I used a mantel test on centered environmental data and the Bray-Curtis distances of the microbial community data. My first question is if the environmental data is used to create a Euclide…
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### Problem Description
when joining on coordinates ATM the quality of matches is assessed with euclidean distances between pairs of (lat, long) coordinates.
it would be much easier and give better …
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I believe there is an issue with the way the routines `euclideanDistanceOnHexagonalPlanarMap` and `euclideanDistanceOnHexagonalToroidMap` are calculating the Euclidean distance between cells on a hexa…
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The quality of an embedding in PyMDE is judged by the collection of of Euclidean distances between pairs of embedding distances.
Euclidean distance is natural for visualization, since it is the dis…