Matrices in Python without Numpy: Part 2
Let's continue from yesterday.
Screenshots are made using an emulator on my.numworks.com.
Matrix Addition
madd(matrix 1, matrix 2)
Adds matrix 1 to matrix 2. The dimensions of both matrices must be the same.
def madd(A,B):
# dimension check
if len(A) != len(B) or len(A[0]) != len(B[0]):
return "Dimensions of A and B mismatch"
else:
M=newmatrix(len(A),len(A[0]))
for i in range(len(A)):
for j in range(len(A[0])):
M[i][j]=A[i][j]+B[i][j]
return M
In this example screen shot:
mat1 = [ [ 8, -2 ], [ -4, 7 ] ]
mat2 = [ [ 1, 3 ], [ 5, 6 ] ]
Matrix addition is communitive. Hence, mat1 + mat2 = mat2 + mat1.
Matrix Multiplication
mmult(mat1, mat2)
Multiplies mat1 to mat2. Here the number of columns of mat1 must be the same as the rows of mat2.
Matrix multiplication is not communitive. Hence, mat1 * mat2 ≠ mat2 * mat1
def mmult(A,B):
# dimension check
if len(A[0]) != len(B):
return "Columns of A != Rows of B"
else:
M=newmatrix(len(A),len(B[0]))
for i in range(len(A)):
for j in range(len(B[0])):
t=0
for k in range(len(A[0])):
t +=A[i][k]*B[k][j]
M[i][j]=t
return M
Note that both madd and mmult require newmatrix. See yesterday's post for the code for newmatrix.
Next time, we will work with determinants and inverses.
Happy computing,
Eddie
Source:
Ives, Thom. "BASIC Linear Algebra Tools in Pure Python without Numpy or Scipy" Integrated Machine Learning & AI December 11, 2018. https://integratedmlai.com/basic-linear-algebra-tools-in-pure-python-without-numpy-or-scipy/
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