3.1 Write a function called matrix_classifier that has one input called Matrix1 represented as a numpy array. The function should classify the matrix as one of (i) one-to-one, (ii) onto, (iii) both (i.e. invertible), or (iv) neither. It should return the classification represented as a string (i.e. either "one-to-one", "onto", "invertible", or "neither").

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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3.1 Write a function called matrix_classifier that has one input called Matrix 1
represented as a numpy array. The function should classify the matrix as one of (i)
one-to-one, (ii) onto, (iii) both (i.e. invertible), or (iv) neither. It should return the
classification represented as a string (i.e. either "one-to-one", "onto", "invertible", or
"neither").
In
Student's answer
import numpy as np
def matrix_classifier (Matrix1):
# Check if the matrix is square
if Matrix1.shape [0] != Matrix1.shape [1]:
return "neither" # Not square, cannot calculate det
# Calculate the determinant of the matrix
det = np. linalg.det (Matrix1)
# Calculate the rank of the matrix
rank = np.linalg.matrix_rank (Matrix1)
if det! 0:
# If the determinant is nonzero, it's invertible
return "invertible"
elif rank
(Top)
Matrix1.shape [1]:
# If the rank is less than the number of columns, it
return "neither"
else:
# If the rank equals the number of columns, check if
if rank Matrix1.shape [0]:
return "onto"
return "one-to-one"
else:
Transcribed Image Text:3.1 Write a function called matrix_classifier that has one input called Matrix 1 represented as a numpy array. The function should classify the matrix as one of (i) one-to-one, (ii) onto, (iii) both (i.e. invertible), or (iv) neither. It should return the classification represented as a string (i.e. either "one-to-one", "onto", "invertible", or "neither"). In Student's answer import numpy as np def matrix_classifier (Matrix1): # Check if the matrix is square if Matrix1.shape [0] != Matrix1.shape [1]: return "neither" # Not square, cannot calculate det # Calculate the determinant of the matrix det = np. linalg.det (Matrix1) # Calculate the rank of the matrix rank = np.linalg.matrix_rank (Matrix1) if det! 0: # If the determinant is nonzero, it's invertible return "invertible" elif rank (Top) Matrix1.shape [1]: # If the rank is less than the number of columns, it return "neither" else: # If the rank equals the number of columns, check if if rank Matrix1.shape [0]: return "onto" return "one-to-one" else:
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