# Function to multiply two matrices without using np.dot
def multiply_matrices(matrix1, matrix2):
if len(matrix1[0]) != len(matrix2):
raise ValueError("Number of columns in matrix1 must be equal to the number of rows in matrix2.")
result = [[0 for _ in range(len(matrix2[0]))] for _ in range(len(matrix1))]
for i in range(len(matrix1)):
for j in range(len(matrix2[0])):
for k in range(len(matrix2)):
result[i][j] += matrix1[i][k] * matrix2[k][j]
return result
# Function to display position and address of each element
def display_position_and_address(matrix):
rows, cols = len(matrix), len(matrix[0])
for i in range(rows):
for j in range(cols):
print(f"Element at position ({i},{j}): {matrix[i][j]}")
print(f"Address of element at position ({i},{j}): {id(matrix[i][j])}")
print()
# Take input for matrix1
rows1 = int(input("Enter the number of rows for matrix 1: "))
cols1 = int(input("Enter the number of columns for matrix 1: "))
print("Enter the elements of matrix 1:")
matrix1 = []
for i in range(rows1):
row = []
for j in range(cols1):
element = int(input(f"Enter the element at position ({i},{j}): "))
row.append(element)
matrix1.append(row)
# Take input for matrix2
rows2 = int(input("Enter the number of rows for matrix 2: "))
cols2 = int(input("Enter the number of columns for matrix 2: "))
print("Enter the elements of matrix 2:")
matrix2 = []
for i in range(rows2):
row = []
for j in range(cols2):
element = int(input(f"Enter the element at position ({i},{j}): "))
row.append(element)
matrix2.append(row)
# Multiply the matrices
try:
result_matrix = multiply_matrices(matrix1, matrix2)
print("\nResult Matrix:")
for row in result_matrix:
print(row)
print("\nPosition and Address of each element in the Result Matrix:")
display_position_and_address(result_matrix)
except ValueError as e:
print(e)
# Function to multiply two matrices without using np.dot
def multiply_matrices(matrix1, matrix2):
if len(matrix1[0]) != len(matrix2):
raise ValueError("Number of columns in matrix1 must be equal to the number of rows in matrix2.")
result = [[0 for _ in range(len(matrix2[0]))] for _ in range(len(matrix1))]
for i in range(len(matrix1)):
for j in range(len(matrix2[0])):
for k in range(len(matrix2)):
result[i][j] += matrix1[i][k] * matrix2[k][j]
return result
# Take input for matrix1
rows1 = int(input("Enter the number of rows for matrix 1: "))
cols1 = int(input("Enter the number of columns for matrix 1: "))
print("Enter the elements of matrix 1:")
matrix1 = []
for i in range(rows1):
row = []
for j in range(cols1):
element = int(input(f"Enter the element at position ({i},{j}): "))
row.append(element)
matrix1.append(row)
# Take input for matrix2
rows2 = int(input("Enter the number of rows for matrix 2: "))
cols2 = int(input("Enter the number of columns for matrix 2: "))
print("Enter the elements of matrix 2:")
matrix2 = []
for i in range(rows2):
row = []
for j in range(cols2):
element = int(input(f"Enter the element at position ({i},{j}): "))
row.append(element)
matrix2.append(row)
# Multiply the matrices
try:
result_matrix = multiply_matrices(matrix1, matrix2)
print("\nResult Matrix:")
for row in result_matrix:
print(row)
except ValueError as e:
print(e)
import numpy as np
# Function to multiply two matrices
def multiply_matrices(matrix1, matrix2):
result = np.dot(matrix1, matrix2)
return result
# Function to display position and address of each element
def display_position_and_address(matrix):
rows, cols = matrix.shape
for i in range(rows):
for j in range(cols):
print(f"Element at position ({i},{j}): {matrix[i][j]}")
print(f"Address of element at position ({i},{j}): {id(matrix[i][j])}")
print()
# Take input for matrix1
rows1 = int(input("Enter the number of rows for matrix 1: "))
cols1 = int(input("Enter the number of columns for matrix 1: "))
print("Enter the elements of matrix 1:")
matrix1 = []
for i in range(rows1):
row = []
for j in range(cols1):
element = int(input(f"Enter the element at position ({i},{j}): "))
row.append(element)
matrix1.append(row)
# Take input for matrix2
rows2 = int(input("Enter the number of rows for matrix 2: "))
cols2 = int(input("Enter the number of columns for matrix 2: "))
print("Enter the elements of matrix 2:")
matrix2 = []
for i in range(rows2):
row = []
for j in range(cols2):
element = int(input(f"Enter the element at position ({i},{j}): "))
row.append(element)
matrix2.append(row)
# Convert lists to numpy arrays
matrix1 = np.array(matrix1)
matrix2 = np.array(matrix2)
# Multiply the matrices
result_matrix = multiply_matrices(matrix1, matrix2)
# Display the result matrix
print("\nResult Matrix:")
print(result_matrix)
# Display position and address of each element in the result matrix
print("\nPosition and Address of each element in the Result Matrix:")
display_position_and_address(result_matrix)
In operating systems, partition allocation methods refer to the techniques used to allocate memory partitions to processes. Two common methods are:
1. Fixed Partition Allocation:
Explanation: In fixed partition allocation, the main memory is divided into fixed-size partitions. Each partition can hold one process, and the size of the partition is determined during system setup or configuration.
Characteristics:
Simple and easy to implement.
However, it may lead to internal fragmentation if a partition is larger than the size of the process it accommodates.
2. Dynamic Partition Allocation:
Explanation: Dynamic partition allocation, also known as variable or dynamic partitioning, involves dividing the memory into variable-sized partitions based on the size of the processes.
Characteristics:
Allows for more efficient use of memory by accommodating processes of varying sizes.
May suffer from external fragmentation, where free memory is scattered in small blocks, making it challenging to allocate contiguous memory for larger processes.
These allocation methods play a crucial role in managing memory efficiently and avoiding wastage. The choice between fixed and dynamic partitioning depends on the system requirements and the nature of the processes it needs to handle.
2)
Comments
Post a Comment