On Sat, Jun 6, 2009 at 12:09 AM, Robert Kern<robert.k...@gmail.com> wrote:
> +1
OK, thanks. I'll try to get it ready.
> diag_indices() can be made more efficient, but these are fine.
Suggestion? Right now it's not obvious to me...
A few more questions:
- Are doctests considered enough testing for numpy, or are separate
tests also required?
- Where should these go?
- Any interest in also having the stuff below? I'm needing to build
structured random arrays a lot (symmetric, anti-symmetric, symmetric
with a particular diagonal, etc), and these are coming in handy. If
you want them, I'll put the whole thing together (these use the
indexing utilities from the previous suggestion).
Thanks!
f
#### Other suggested utilities. Not fully commented yet, but if they
are wanted for numpy, will be submitted in final form.
def structured_rand_arr(size, sample_func=np.random.random,
ltfac=None, utfac=None, fill_diag=None):
"""Make a structured random 2-d array of shape (size,size).
Parameters
----------
size : int
Determines the shape of the output array: (size,size).
sample_func : function, optional.
Must be a function which when called with a 2-tuple of ints, returns a
2-d array of that shape. By default, np.random.random is used, but any
other sampling function can be used as long as matches this API.
utfac : float, optional
Multiplicative factor for the lower triangular part of the matrix.
ltfac : float, optional
Multiplicative factor for the lower triangular part of the matrix.
fill_diag : float, optional
If given, use this value to fill in the diagonal. Otherwise the diagonal
will contain random elements.
"""
# Make a random array from the given sampling function
mat0 = sample_func((size,size))
# And the empty one we'll then fill in to return
mat = np.empty_like(mat0)
# Extract indices for upper-triangle, lower-triangle and diagonal
uidx = triu_indices(size,1)
lidx = tril_indices(size,-1)
didx = diag_indices(size)
# Extract each part from the original and copy it to the output, possibly
# applying multiplicative factors. We check the factors instead of
# defaulting to 1.0 to avoid unnecessary floating point multiplications
# which could be noticeable for very large sizes.
if utfac:
mat[uidx] = utfac * mat0[uidx]
else:
mat[uidx] = mat0[uidx]
if ltfac:
mat[lidx] = itfac * mat0.T[lidx]
else:
mat[lidx] = mat0.T[lidx]
# If fill_diag was provided, use it; otherwise take the values in the
# diagonal from the original random array.
if fill_diag:
mat[didx] = fill_diag
else:
mat[didx] = mat0[didx]
return mat
def symm_rand_arr(size,sample_func=np.random.random,fill_diag=None):
"""Make a symmetric random 2-d array of shape (size,size).
Parameters
----------
n : int
Size of the output array.
fill_diag : float, optional
If given, use this value to fill in the diagonal. Useful for
"""
return structured_rand_arr(size,sample_func,fill_diag=fill_diag)
def antisymm_rand_arr(size,sample_func=np.random.random,fill_diag=None):
"""Make an anti-symmetric random 2-d array of shape (size,size).
Parameters
----------
n : int
Size of the output array.
"""
return structured_rand_arr(size,sample_func,ltfac=-1.0,fill_diag=fill_diag)
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