Distance-Measures¶

chebyshev - (chebyshev)¶

The distance is calculated as $d(x,y) = \max_{i} |x_i-y_i|$ .

euclidean - (euclidean)¶

The distance is calculated as $d(x,y) = \sqrt{\sum_{i=0}^n (x_i-y_i)^2}$ .

hamming - (hamming)¶

The distance is calculated as the number of components which differ over the number of components.

manhattan - (manhattan)¶

The distance is calculated as $d(x,y) = \sum_{i=0}^n |x_i-y_i|$ .

minkowski - (minkowski)¶

The distance is calculated as $d(x,y) = (\sum_{i=0}^n (x_i-y_i)^p)^{1/p}$ .

Parameters¶

p - Parameter of the Minkowski distance (exponent).¶

name: p
min: 0.001
default:
type: float

seuclidean - (seuclidean)¶

The distance is calculated as $d(x,y) = \sqrt{\frac{\sum_{i=0}^n (x_i-y_i)^2}{s^2_i}}$ , where $s^2_i$ is the standard deviation between the $x_i$ and $y_i$ in the data set.

sqeuclidean - (sqeuclidean)¶

The distance is calculated as $d(x,y) = \sum_{i=0}^n (x_i-y_i)^2$ .

weuclidean - (weuclidean)¶

The distance is calculated as $d(x,y) = \sqrt{\sum_{i=0}^n w_i(x_i-y_i)^2}$ .

Parameters¶

weights - Comma-separated list of weights for each dimension.¶

name: Weigths
default:
type: string

wminkowski - (wminkowski)¶

The distance is calculated as $d(x,y) = (\sum_{i=0}^n (x_i-y_i)^p)^{1/p}$ .

Parameters¶

p - Parameter of the Minkowski distance (exponent).¶

name: p
min: 0.001
default:
type: float

weights - Comma-separated list of weights for each dimension.¶

name: Weights
default:
type: string