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Fixed error in matrix multiplication that didn't multiple non-square … #898

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Fixed error in matrix multiplication that didn't multiple non-square … #898

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Fixed error in matrix multiplication that didn't multiple non-square …
internetdhruv Jun 11, 2019
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Merge remote-tracking branch 'upstream/master'
internetdhruv Jun 12, 2019
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15 changes: 11 additions & 4 deletions matrix/matrix_multiplication_addition.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,12 +15,19 @@ def scalarMultiply(matrix , n):

def multiply(matrix_a, matrix_b):
matrix_c = []
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with numpy arrays, no need to define matrix_c at the beginning of the function. Also, since I can't access the beginning of this module would need to add "import numpy as np".

Below is a suggestion to reduce the number of code lines. Maybe I'm missing the point of this module, but using numpy would be a lot more efficient. I know the rest of the doc has no docstring, but I figured I would add one in the suggestion.

You can the entire function with just 8 lines of code.

Suggested change
matrix_c = []
"""
:param matrix_a: matrix as a list or array, 1st matrix that defines #columns for matrix rule [a1 x a2]
:type matrix_a: list of int or list of float
:param matrix_b: matrix as a list or array, 2nd matrix that defines #rows for matrix rule [b1 x b2]
:type matrix_b: list of int or list of float
:return: numpy array [a1 x b2]
"""
matrix_a = np.array(matrix_a)
matrix_b = np.array(matrix_b)
try:
matrix_c = np.dot(matrix_a, matrix_b)
return matrix_c
except ValueError:
raise

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This definitely works. In fact, a lot of stuff in this project can be done using in-built/library functions, but I think the idea of this project is to show how the core algorithms work.

Using numpy.dot would definitely make it work in lesser lines of code, and possibly even be faster, but that level of abstraction would hide what exactly is happening.

I may be wrong though, and if what we're going for is smaller code and speed, your suggestion would work (Although I think numpy.matmul would be a better choice)

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@StephenGemin StephenGemin Jun 12, 2019 •
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That makes sense. You're right on both points ☺. Best leave your initial commit than.

I never heard of numpy.matmul, now I know.
https://www.numpy.org/devdocs/reference/generated/numpy.dot.html

TLDR: If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred.

Appreciate the feedback!

n = len(matrix_a)
for i in range(n):
num_rows_a = len(matrix_a)
num_cols_a = len(matrix_a[0])
num_rows_b = len(matrix_b)
num_cols_b = len(matrix_b[0])

if num_cols_a != num_rows_b :
raise ValueError('Cannot multiply matrix of dimensions {},{} and {},{}'.format(num_rows_a,num_cols_a,num_rows_b,num_cols_b))

for i in range(num_rows_a):
list_1 = []
for j in range(n):
for j in range(num_cols_b):
val = 0
for k in range(n):
for k in range(num_rows_a):
val = val + matrix_a[i][k] * matrix_b[k][j]
list_1.append(val)
matrix_c.append(list_1)
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