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- coordinates_to_euclideandistance(X)
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returns a square matrix of Euclidean distances for the points given in
X.
- cy_distance(v1, v2, weight=0.10000000000000001)
-
Yong`s dissimilarity measure.
- distance_matrix(M, measure=<function euclidean_distance>, mode='byrow', returnFull=1)
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- euclidean_distance(v1, v2, axis=0)
-
Euclidean distance between the two arrays v1 and v2:
- euclidean_norm = euclidean_distance(v1, v2, axis=0)
-
Euclidean distance between the two arrays v1 and v2:
- jaccard_distance(v1, v2)
-
Jaccard similarity converted to dissimilarity.
- jaccard_similarity(v1, v2)
-
Jaccard similarity index J = c / (a + b + c) with
c = species shared in both sites,
a,b = species unique to each site
- manhattan_distance(v1, v2, axis=0)
-
Manhattan distance between the two arrays v1 and v2:
- soerensen_distance(v1, v2)
-
Soerensen similarity conveted to dissimilarity
- soerensen_similarity(v1, v2)
-
Soerensen similarity index SJ = c / (A + B - c) with
c = species shared in both sites
A,B = total number of species in each site
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