### Introduction:

*It computes minimum distances between one point and a set of points. *This function computes for each row in X, the index of the row of Y which is closest (according to the specified distance). *This function works with dense 2D arrays only.

### Parameter:

*X - array like structure.
*Y - array like structure.

### Returns:

*Returns minimum indices array.

### Implementation of Pairwise Distances Argmin:

from math import *
import numpy as np

class MinDis:

def dii(self,a,b):

s=0
for i in range(len(a)):
s += (a[i] - b[i]) * (a[i] - b[i])

return sqrt(s)

def midis_calc(self,l1,l2):

ans = []
for j in range(len(l1)):

x = self.dii(l1[j],l2[0])
idx = 0

for i in range(1,len(l2)):
y = self.dii(l1[j],l2[i])
if y<x:
idx = i

ans.append(idx)

print(ans)

def main():

dis = MinDis()
l1 = [[1,2],
[4,5]]
l2 = [[1,2],
[4,5]]
dis.midis_calc(l1,l2)

if __name__=="__main__":
main()

[0, 1]

### Using Inbuilt Library:

from sklearn.metrics import pairwise_distances_argmin
l1 = [[1,2],
[4,5]]
l2 = [[1,2],
[4,5]]
print(pairwise_distances_argmin(l1,l2))

### Output:

[0, 1]

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