Random Matrix Transpose by Specified Rows

This function takes a 2D tensor and a number of rows as input. It first checks the validity of the input, then transposes the matrix, and returns a new matrix where the transposed part is the top num_rows rows of the original matrix.

Technology Stack : PyTorch, NumPy

Code Type : Function

Code Difficulty : Intermediate

                
                    
import torch
import numpy as np

def random_matrix_transpose(matrix, num_rows):
    """
    This function takes a 2D tensor and transposes it by a specified number of rows.
    """
    if not isinstance(matrix, torch.Tensor):
        raise TypeError("Input matrix must be a torch.Tensor")
    
    if not isinstance(num_rows, int) or num_rows <= 0:
        raise ValueError("Number of rows must be a positive integer")
    
    # Transpose the matrix
    transposed_matrix = matrix.transpose(0, 1)
    
    # Slice the first num_rows to get the top left block
    top_left_block = transposed_matrix[:num_rows, :]
    
    # Slice the rest to get the bottom right block
    bottom_right_block = transposed_matrix[num_rows:, :]
    
    # Concatenate the top left block with the original bottom right block
    result_matrix = torch.cat((top_left_block, bottom_right_block), dim=0)
    
    return result_matrix