stevenrouk · August 25, 2019 22:50
diff --git a/for_loop_matrix_multiplication3.py b/for_loop_matrix_multiplication3.py
 import numpy as np

 def for_loop_matrix_multiplication3(A, B):
    """Third version of a for loop matrix multiplication.
    
    In this version, we replace the NumPy arrays with lists
    and figure out how to store the resulting dot product values
    in the correct places for the new matrix."""
    # We're going to leave these as NumPy arrays for now because we're
    # still transposing B using the "B.T" functionality of np.array.
    A = np.array(A)
    B = np.array(B)

    # Instead of a NumPy array, we're just going to use lists now!
    new_matrix = []

    for i, row in enumerate(A):
        new_row = []
        for j, col in enumerate(B.T):
            dot_product = sum([x*y for (x, y) in zip(row, col)])
            # Appending this value to the list is essentially storing
            # a new column value for the same row in our new matrix.
            # So the new_row list represents a row in the new matrix,
            # and we add values to that row one by one as we multiply
            # our row in A by each column in B.
            new_row.append(dot_product)
        
        # Now, we need to append the new_row to our new_matrix
        # before moving on to the next row in A.
        new_matrix.append(new_row)
    
    return new_matrix
	import numpy as np

	def for_loop_matrix_multiplication3(A, B):
	"""Third version of a for loop matrix multiplication.

	In this version, we replace the NumPy arrays with lists
	and figure out how to store the resulting dot product values
	in the correct places for the new matrix."""
	# We're going to leave these as NumPy arrays for now because we're
	# still transposing B using the "B.T" functionality of np.array.
	A = np.array(A)
	B = np.array(B)

	# Instead of a NumPy array, we're just going to use lists now!
	new_matrix = []

	for i, row in enumerate(A):
	new_row = []
	for j, col in enumerate(B.T):
	dot_product = sum([x*y for (x, y) in zip(row, col)])
	# Appending this value to the list is essentially storing
	# a new column value for the same row in our new matrix.
	# So the new_row list represents a row in the new matrix,
	# and we add values to that row one by one as we multiply
	# our row in A by each column in B.
	new_row.append(dot_product)

	# Now, we need to append the new_row to our new_matrix
	# before moving on to the next row in A.
	new_matrix.append(new_row)

	return new_matrix
No results found