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Numpy distance between two points

Euclidean distance python

# I hope to be of help and to have understood the request
from math import sqrt # import square root from the math module
# the x and y coordinates are the points on the Cartesian plane
pointA = (x, y) # first point
pointB = (x, y) # second point
distance = calc_distance(pointA, pointB) # here your beautiful result
def calc_distance(p1, p2): # simple function, I hope you are more comfortable
  return sqrt((p1[0]-p2[0])**2+(p1[1]-p2[1])**2) # Pythagorean theorem

Distance euc of two arrays python

# Use numpy.linalg.norm:
import numpy as np

a = np.array([1.0, 3.5, -6.3])
b = np.array([4.5, 1.6,  1.2])

dist = np.linalg.norm(a-b)

Numpy distance between two points

dist = numpy.linalg.norm(a-b)

Source: stackoverflow.com

Distance between numpy arrays

dist = numpy.linalg.norm(a-b, ord=2) #ord=2 is default and means Euclidean distance, but I'm showing here that you can specify it

Source: stackoverflow.com

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