Did I find the right examples for you? yes no      Crawl my project      Python Jobs

# cholesky_band.cholesky_band

All Samples(1)  |  Call(0)  |  Derive(0)  |  Import(1)
```Compute Cholesky decomposition of banded matrix.

Parameters
----------
l : ndarray
A matrix on which to perform the Cholesky decomposition.
mininf : float, optional
Entries in the `l` matrix are considered negative if they are less
than this value (default 0.0).
verbose : bool, optional(more...)
```

```        def cholesky_band(l,mininf=0.0,verbose=False):
"""Compute Cholesky decomposition of banded matrix.

Parameters
----------
l : ndarray
A matrix on which to perform the Cholesky decomposition.
mininf : float, optional
Entries in the `l` matrix are considered negative if they are less
than this value (default 0.0).
verbose : bool, optional
If set to ``True``, print some debugging information.

Returns
-------
cholesky_band : tuple
If problems were detected, the first item will be the index or
indexes where the problem was detected, and the second item will simply
be the input matrix.  If no problems were detected, the first item
will be -1, and the second item will be the Cholesky decomposition.
"""
import numpy as np
lower = l.copy()
bw,nn = lower.shape
n = nn - bw
if verbose:
print(lower[0,0:n])
negative = lower[0,0:n] <= mininf
if negative.any() or not np.all(np.isfinite(lower)):
if verbose:
print(negative.nonzero())
return (negative.nonzero(),l)
kn = bw - 1
spot = np.arange(kn,dtype='i4') + 1
bi = np.arange(kn,dtype='i4')
for i in range(1,kn):
bi = np.append(bi, np.arange(kn-i,dtype='i4') + (kn+1)*i)
for j in range(n):
lower[0,j] = np.sqrt(lower[0,j])
lower[spot,j] /= lower[0,j]
x = lower[spot,j]
if not np.all(np.isfinite(x)):
if verbose:
print('NaN found in cholesky_band.')
return (j,l)
hmm = np.outer(x,x)
here = bi+(j+1)*bw
lower.T.flat[here] -= hmm.flat[bi]
return (-1,lower)
```

```"""