Euclidean distance using Numpy

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dhupee answered on June 6, 2020 Popularity 9/10 Helpfulness 2/10

answer Euclidean distance using Numpy

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Euclidean distance using Numpy

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# Python code to find Euclidean distance
# using dot()
 
import numpy as np
 
# initializing points in
# numpy arrays
point1 = np.array((1, 2, 3))
point2 = np.array((1, 1, 1))
 
# subtracting vector
temp = point1 - point2
 
# doing dot product
# for finding
# sum of the squares
sum_sq = np.dot(temp.T, temp)
 
# Doing squareroot and
# printing Euclidean distance
print(np.sqrt(sum_sq))

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Contributed on Mar 27 2023

dhupee

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import numpy as np
a = np.array((1,1,1))
b = np.array((2,2,2))
dist = np.linalg.norm(a-b)

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Source: stackoverflow.com

Tags: euclidean-distance euclidean-distanc

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Contributed on May 05 2021

Lovely Lark

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euclidean distance python

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# 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

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Contributed on Jun 06 2020

Envious Eland

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numpy euclidean distance

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

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Source: stackoverflow.com

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Contributed on Dec 08 2020

Awful Albatross

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python euclidean distance

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def euclidean_distance(row1, row2):
    distance = 0.0

    for i in range(len(row1)-1):
        distance += (row1[i] - row2[i])**2
    return m.sqrt(distance)

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Contributed on Oct 16 2022

Muhammad Usman Shakeel

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Euclidean distance using Numpy

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