Answered: [Python (py3)] The code below is for…

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

Section: Chapter Questions

Problem 1PE

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Question

[Python (py3)]

The code below is for matrix operations (addition, multiplication, scalar multiplication and transposition). Please annotate what happens in EACH line so that I can fully understand the code below. Some lines are already annotated.

Please annotate EACH line. Do not just copy the code below and take it as the answer without modifying anything. I already encountered such case many times before.

Sample input 1:
add
2 3
53 -4 1
7 31 2
2 3
67 2 2
-34 6 3

Sample output 1:
120 -2 3
-27 37 5

________________________________________________________________________________

Sample input 2:
scalMultiply
2 2
53 -4
7 31
2

Sample output 2:
106 -8
14 62

_______________________________________________________________________
Sample input 3:
multiply
3 3
34 10 3
7 8 34
6 2 12
3 2
1 2
3 4
5 6

Sample output 3:
79 126
201 250
72 92
_______________________________________________________________________
Sample input 4:
transpose
3 3
34 10 3
7 8 34
6 2 12

Sample output 4:
34 7 6
10 8 2
3 34 12

------------------------------------------------------------------------------

import numpy as np

import sys

# function to add

def add(lines):

# finding the dimesions

dim = lines[1].split()

k=2

mat1 = np.empty((0,int(dim[0])), int)

for i in range(0,int(dim[0])):

l = lines[k].split()

l = list(map(int, l))

ls = np.array([l])

k += 1

# adding the data in one row

mat1 = np.append(mat1, ls)

# reshaping the into 2D array with given row and column

mat1 = mat1.reshape(int(dim[0]),int(dim[1]))

dim2 = lines[k].split()

k += 1

mat2 = np.empty((0,int(dim2[0])), int)

for i in range(0,int(dim2[0])):

l = lines[k].split()

l = list(map(int, l))

ls = np.array([l])

k += 1

# adding the data in one row

mat2 = np.append(mat2, ls)

# reshaping the into 2D array with given row and column

mat2 = mat2.reshape(int(dim2[0]),int(dim2[1]))

# comparing the dimensions of the matrix

if dim != dim2:

sys.exit("Matrix addition cannot be performed; dimensions are unequal.")

# returning the resultant matrix

return np.add(mat1,mat2)

# function to multiply

def multiply(lines):

# finding the dimesions

dim = lines[1].split()

k=2

mat1 = np.empty((0,int(dim[0])), int)

for i in range(0,int(dim[0])):

l = lines[k].split()

l = list(map(int, l))

ls = np.array([l])

k += 1

# adding the data in one row

mat1 = np.append(mat1, ls)

# reshaping the into 2D array with given row and column

mat1 = mat1.reshape(int(dim[0]),int(dim[1]))

dim2 = lines[k].split()

k += 1

mat2 = np.empty((0,int(dim2[0])), int)

for i in range(0,int(dim2[0])):

l = lines[k].split()

l = list(map(int, l))

ls = np.array([l])

k += 1

# adding the data in one row

mat2 = np.append(mat2, ls)

# reshaping the into 2D array with given row and column

mat2 = mat2.reshape(int(dim2[0]),int(dim2[1]))

# comparing the col of matrix 1 with row of matrix 2

if int(dim[1]) != int(dim2[0]):

sys.exit("Matrix multiplication cannot be performed; number of columns of Matrix A is not equal to number of rows of Matrix B.")

# returning the product of 2 matrices

return np.dot(mat1,mat2)

# function to do scalar multiplication

def scalmultiply(lines):

# finding the dimesions

dim = lines[1].split()

k=2

mat = np.empty((0,int(dim[0])), int)

for i in range(0,int(dim[0])):

l = lines[k].split()

l = list(map(int, l))

ls = np.array([l])

k += 1

# adding the data in one row

mat = np.append(mat, ls)

# reshaping the into 2D array with given row and column

mat = mat.reshape(int(dim[0]),int(dim[1]))

# finding the number to be mulitplied with

num = lines[k].split()

return np.multiply(mat,int(num[0]))

# function to tranpose the matrix

def transpose(lines):

# finding the dimesions

dim = lines[1].split()

k=2

mat = np.empty((0,int(dim[0])), int)

for i in range(0,int(dim[0])):

l = lines[k].split()

l = list(map(int, l))

ls = np.array([l])

k += 1

# adding the data in one row

mat = np.append(mat, ls)

# reshaping the into 2D array with given row and column

mat = mat.reshape(int(dim[0]),int(dim[1]))

# returning the matrix by transposing

return np.transpose(mat)

# main block

f1 = open("file1.txt","r")

lines = f1.readlines()

value = lines[0].split()

# checking the operation and calling the function

if value[0] == "add":

s = add(lines)

if value[0] == "multiply":

s = multiply(lines)

if value[0] == "scalMultiply":

s = scalmultiply(lines)

if value[0] == "transpose":

s = transpose(lines)

# opening the file in write mode

f2 = open("output.txt","w")

# write output matrix in output.txt file

for i in range(0, len(s)):

for j in range(0, len(s[i])):

line = str(s[i][j])+" "

f2.write(line)

f2.write("\n")

# close the output file

f2.close()

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