Numpy matrx and arrary simple calculation

Question

I need to do simple calculation on matricies and vector using np.array and np.matrix. My problem is that I do not know what is the difference between np.array and np.matrix. Calculation do I need to do is (among others):

A - matrix
B - matrix
x - vector
1. A * x
2. A * B
3. (A * A^T)^-1

Once, I need to do in using np.array and again using np.matrix. I have tried many ways like:

Ax = A.dot(x)  # multiplication by vector
AB = np.dot(A, B) # multiplication by matrix
ab = np.matmul(A, B) # multiplication by matrix
At = A.transpose()
At = np.transpose(A)
inv = np.linalg.inv(a) # and inverse matrix, but I do not know which is np. matrix or np.array.

Kindly help.

The task is that I do not have an example, I just have to know the difference on indifferent A and B — ariac, Mar 16 '22 at 10:45
`matrix` is deprecated, see the [doc](https://numpy.org/doc/stable/reference/generated/numpy.matrix.html) — mozway, Mar 16 '22 at 10:48
@hpauli I need to calculate multiplication, transpose and inversion in two ways, that is np.array and np.matrix and I do not now what is the difference and how to differ it. — ariac, Mar 16 '22 at 16:35

score 1 · Answer 1 · answered Mar 16 '22 at 10:38

1

Here's an answer clarifying the differences and the advantages of vectors and matrices. It also explains multiplications between them.

answered Mar 16 '22 at 10:38

elPecos

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Numpy matrx and arrary simple calculation

1 Answers1