PINE LIBRARY
FunctionMinkowskiDistance
Library "FunctionMinkowskiDistance"
Method for Minkowski Distance,
The Minkowski distance or Minkowski metric is a metric in a normed vector space
which can be considered as a generalization of both the Euclidean distance and
the Manhattan distance.
It is named after the German mathematician Hermann Minkowski.
reference: en.wikipedia.org/wiki/Minkowski_distance
double(point_ax, point_ay, point_bx, point_by, p_value) Minkowsky Distance for single points.
Parameters:
point_ax: float, x value of point a.
point_ay: float, y value of point a.
point_bx: float, x value of point b.
point_by: float, y value of point b.
p_value: float, p value, default=1.0(1: manhatan, 2: euclidean), does not support chebychev.
Returns: float
ndim(point_x, point_y, p_value) Minkowsky Distance for N dimensions.
Parameters:
point_x: float array, point x dimension attributes.
point_y: float array, point y dimension attributes.
p_value: float, p value, default=1.0(1: manhatan, 2: euclidean), does not support chebychev.
Returns: float
Method for Minkowski Distance,
The Minkowski distance or Minkowski metric is a metric in a normed vector space
which can be considered as a generalization of both the Euclidean distance and
the Manhattan distance.
It is named after the German mathematician Hermann Minkowski.
reference: en.wikipedia.org/wiki/Minkowski_distance
double(point_ax, point_ay, point_bx, point_by, p_value) Minkowsky Distance for single points.
Parameters:
point_ax: float, x value of point a.
point_ay: float, y value of point a.
point_bx: float, x value of point b.
point_by: float, y value of point b.
p_value: float, p value, default=1.0(1: manhatan, 2: euclidean), does not support chebychev.
Returns: float
ndim(point_x, point_y, p_value) Minkowsky Distance for N dimensions.
Parameters:
point_x: float array, point x dimension attributes.
point_y: float array, point y dimension attributes.
p_value: float, p value, default=1.0(1: manhatan, 2: euclidean), does not support chebychev.
Returns: float
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