# Python Program to Implement Numpy Arrays Assignment Solution.

## Instructions

Objective
Write a program to implement numpy arrays in python.

## Requirements and Specifications Source Code

```import numpy as np def closest_point(A1, A2): # Convert arrays to float64 A1 = A1.astype('float64') A2 = A2.astype('float64') # First, check that the number of columns in both arrays is the same if A1.shape != A2.shape: raise ValueError("Arrays must have the same number of columns") # Compute the pair-wise difference between each row from A1 and A2 # Check if the matrices has same number of rows if A1.shape != A2.shape: diff = A1[np.newaxis,:,:]-A2[:,np.newaxis,:] else: diff = A1-A2 # Now compute the distance. The distance is calculated using the Euclidean Distance formula dist = np.sqrt(np.sum(diff**2,axis=-1)) # Now, get the location of the minimum distance loc = np.where(dist == dist.min()) if A1.shape != A2.shape: pos = loc else: pos = loc # The location contains two values: row and column. The row index # is the point in A1 and the column index is the index in A2 return pos # Test if __name__ == '__main__': # Generate two arrays of random numbers #A1 = np.random.rand(150,2) #A2 = np.random.rand(500,2) A1 = np.array([[0,0],[1,1],[0,1],[1,0]]) A2 = np.array([[2,2],[2,3],[3,2],[3,3], [0.25,2]]) B1 = np.arange(10**6).reshape(10**4, 10**2) B2 = np.array( * 10**6).reshape((10**4, 10**2)) # Compute pos = closest_point(B1,B2) print(pos)```