I have this line of code in MATLAB, written by someone else:

```
c=a.'/b
```

I need to translate it into Python. a, b, and c are all arrays. The dimensions that I am currently using to test the code are:

a: 18x1,

b: 25x18,

which gives me c with dimensions 1x25.

The arrays are not square, but I would not want the code to fail if they were. Can someone explain exactly what this line is doing (mathematically), and how to do it in Python? (i.e., the equivalent for the built-in mrdivide function in MATLAB if it exists in Python?)

The line

```
c = a.' / b
```

computes the solution of the equation *c b = a ^{T}* for

```
c = numpy.linalg.lstsq(b.T, a.T)[0].T
```

The symbol `/`

is the matrix right division operator in MATLAB, which calls the `mrdivide`

function. From the documentation, matrix right division is related to matrix left division in the following way:

```
B/A = (A'\B')'
```

If `A`

is a square matrix, `B/A`

is roughly equal to `B*inv(A)`

(although it's computed in a different, more robust way). Otherwise, `x = B/A`

is the solution in the least squares sense to the under- or over-determined system of equations `x*A = B`

. More detail about the algorithms used for solving the system of equations is given here. Typically packages like LAPACK or BLAS are used under the hood.

The NumPy package for Python contains a routine `lstsq`

for computing the least-squares solution to a system of equations. This routine will likely give you comparable results to using the `mrdivide`

function in MATLAB, but it is unlikely to be *exact*. Any differences in the underlying algorithms used by each function will likely result in answers that differ slightly from one another (i.e. one may return a value of 1.0, whereas the other may return a value of 0.999). The relative size of this error *could* end up being larger, depending heavily on the specific system of equations you are solving.

To use `lstsq`

, you may have to adjust your problem slightly. It appears that you want to solve an equation of the form **cB = a**, where **B** is 25-by-18, **a** is 1-by-18, and **c** is 1-by-25. Applying a transpose to both sides gives you the equation **B ^{T}c^{T} = a^{T}**, which is a more standard form (i.e.

`lstsq`

should be (in this order) `lstsq`

should return a 25-element array (*Note: while NumPy doesn't make any distinction between a 1-by-N or N-by-1 array, MATLAB certainly does, and will yell at you if you don't use the proper one.*

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