Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN: 9780133594140
Author: James Kurose, Keith Ross
Publisher: PEARSON
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CREATE A FLOWCHART WITH THE GIVEN CODES BELOW

def mat_desc (mat):
sq - False
mat = np.array (mat)
print (mat)
print ('Shape:', mat.shape)
print ('Size:', mat.size)
print ('Rank:', np.linalg.matrix_rank(mat))
if (mat.shape[0] == mat.shape[1]):
sq - True
print('The matrix is square')
else:
print('The matrix is non-square')
if (mat.shape[0] == 0 and mat.shape[1] == 0):
print('The matrix is empty')
else:
print('The matrix is not empty')
iden = np.identity(mat.shape[0])
if(sq and (iden
print('The matrix is an identity matrix')
mat).all()):
else:
print('The matrix is not an identity matrix')
one = np.ones ( (mat.shape[0], mat.shape[1]))
if((one =- mat).all()):
print ('The matrix is an ones matrix')
else:
print ('The matrix is not an ones matrix')
zero = np.zeros ( (mat.shape[0], mat.shape[1] ))
if((zero == mat).all()):
print('The matrix is an zeros matrix')
else:
print('The matrix is not a zeros matrix')
G - np. array (I
[1,7,8],
[2,2,2],
[4,6,7]
])
mat_desc (G)
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Transcribed Image Text:def mat_desc (mat): sq - False mat = np.array (mat) print (mat) print ('Shape:', mat.shape) print ('Size:', mat.size) print ('Rank:', np.linalg.matrix_rank(mat)) if (mat.shape[0] == mat.shape[1]): sq - True print('The matrix is square') else: print('The matrix is non-square') if (mat.shape[0] == 0 and mat.shape[1] == 0): print('The matrix is empty') else: print('The matrix is not empty') iden = np.identity(mat.shape[0]) if(sq and (iden print('The matrix is an identity matrix') mat).all()): else: print('The matrix is not an identity matrix') one = np.ones ( (mat.shape[0], mat.shape[1])) if((one =- mat).all()): print ('The matrix is an ones matrix') else: print ('The matrix is not an ones matrix') zero = np.zeros ( (mat.shape[0], mat.shape[1] )) if((zero == mat).all()): print('The matrix is an zeros matrix') else: print('The matrix is not a zeros matrix') G - np. array (I [1,7,8], [2,2,2], [4,6,7] ]) mat_desc (G)
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