raptor96 - 4 months ago 19x

Python Question

I have a python code which I am trying to convert to Matlab code. The code is for baseline correction for a wave.

`def baseline_als(y, lam, p, niter=20):`

L = len(y)

D = sparse.csc_matrix(np.diff(np.eye(L), 2))

w = np.ones(L)

for i in xrange(niter):

W = sparse.spdiags(w, 0, L, L)

Z = W + lam * D.dot(D.transpose())

z = spsolve(Z, w*y)

w = p * (y > z) + (1-p) * (y < z)

return z

I have tried it converting like this.

`function [z] = baseline_als(y, lam, p, niter=20)`

L = len(y)

D = sparse.csc_matrix(diff(eye(L), 2))

w = ones(L)

for i = 1:niter

W = sparse.spdiags(w, 0, L, L) %Not working

Z = W + lam * dot(D,transpose(D))

z = spsolve(Z, w*y) % Not working

w = p * (y > z) + (1-p) * (y < z)

end % End of For loop

end % End of function

However there are no functions named

`spsolve`

`spdiag`

Answer

Its quite easy *if you know what spsolve does*. Lets focus on that, as `spidiag`

seems easier to solve, doesn't it?

`spsolve`

"Solve the sparse linear system Ax=b, where b may be a vector or a matrix."

This is exactly what MATLABs`\`

or `mldivide`

does, it solves a system of Ax=b, for x. Happily for you, MATLAB can deal with both sparse and dense matrix with the same function, thus the change shoudl be as easy as:

from:

```
z = spsolve(Z, w*y)
```

to:

```
z= Z\(w*y);
```

Source (Stackoverflow)

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