Hi All, This is with reference to issue #5626 <https://github.com/numpy/numpy/issues/5626>. Currently linalg.norm converts the input like so `x = asarray(x)`. This can produce integer arrays, which in turn may create problems of overflow, or the failure of the abs functions for minimum values of signed integer types. I propose to convert the input to a minimum precision of float32. However, this will be a change in behavior. I'd guess that that might not be much of a problem, as otherwise it is likely that this problem would have been reported earlier. Thoughts? Chuck
On Tue, Mar 3, 2015 at 4:11 PM, Charles R Harris <charlesr.harris@gmail.com> wrote:
Hi All,
This is with reference to issue #5626 <https://github.com/numpy/numpy/issues/5626>. Currently linalg.norm converts the input like so `x = asarray(x)`. This can produce integer arrays, which in turn may create problems of overflow, or the failure of the abs functions for minimum values of signed integer types. I propose to convert the input to a minimum precision of float32. However, this will be a change in behavior. I'd guess that that might not be much of a problem, as otherwise it is likely that this problem would have been reported earlier.
Thoughts?
Not sure if it makes sense here, but elsewhere (I think it was polyval) we let object arrays through unchanged. Jaime -- (\__/) ( O.o) ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes de dominación mundial.
On Tue, Mar 3, 2015 at 5:21 PM, Jaime Fernández del Río < jaime.frio@gmail.com> wrote:
On Tue, Mar 3, 2015 at 4:11 PM, Charles R Harris < charlesr.harris@gmail.com> wrote:
Hi All,
This is with reference to issue #5626 <https://github.com/numpy/numpy/issues/5626>. Currently linalg.norm converts the input like so `x = asarray(x)`. This can produce integer arrays, which in turn may create problems of overflow, or the failure of the abs functions for minimum values of signed integer types. I propose to convert the input to a minimum precision of float32. However, this will be a change in behavior. I'd guess that that might not be much of a problem, as otherwise it is likely that this problem would have been reported earlier.
Thoughts?
Not sure if it makes sense here, but elsewhere (I think it was polyval) we let object arrays through unchanged.
That would still work. I'm thinking something like x = asarray(x) dt = result_type(x, np.float32) if x.dtype.type is not dt.type: x = x.astype(dt) Chuck
On Tue, Mar 3, 2015 at 5:31 PM, Charles R Harris <charlesr.harris@gmail.com> wrote:
On Tue, Mar 3, 2015 at 5:21 PM, Jaime Fernández del Río < jaime.frio@gmail.com> wrote:
On Tue, Mar 3, 2015 at 4:11 PM, Charles R Harris < charlesr.harris@gmail.com> wrote:
Hi All,
This is with reference to issue #5626 <https://github.com/numpy/numpy/issues/5626>. Currently linalg.norm converts the input like so `x = asarray(x)`. This can produce integer arrays, which in turn may create problems of overflow, or the failure of the abs functions for minimum values of signed integer types. I propose to convert the input to a minimum precision of float32. However, this will be a change in behavior. I'd guess that that might not be much of a problem, as otherwise it is likely that this problem would have been reported earlier.
Thoughts?
Not sure if it makes sense here, but elsewhere (I think it was polyval) we let object arrays through unchanged.
That would still work. I'm thinking something like
x = asarray(x) dt = result_type(x, np.float32) if x.dtype.type is not dt.type: x = x.astype(dt)
I'd actually like to add a `min_dtype` keyword to asarray, We need it in several places. Chuck
On Wed, Mar 4, 2015 at 1:34 AM, Charles R Harris <charlesr.harris@gmail.com> wrote:
On Tue, Mar 3, 2015 at 5:31 PM, Charles R Harris < charlesr.harris@gmail.com> wrote:
On Tue, Mar 3, 2015 at 5:21 PM, Jaime Fernández del Río < jaime.frio@gmail.com> wrote:
On Tue, Mar 3, 2015 at 4:11 PM, Charles R Harris < charlesr.harris@gmail.com> wrote:
Hi All,
This is with reference to issue #5626 <https://github.com/numpy/numpy/issues/5626>. Currently linalg.norm converts the input like so `x = asarray(x)`. This can produce integer arrays, which in turn may create problems of overflow, or the failure of the abs functions for minimum values of signed integer types. I propose to convert the input to a minimum precision of float32. However, this will be a change in behavior. I'd guess that that might not be much of a problem, as otherwise it is likely that this problem would have been reported earlier.
Thoughts?
Not sure if it makes sense here, but elsewhere (I think it was polyval) we let object arrays through unchanged.
That would still work. I'm thinking something like
x = asarray(x) dt = result_type(x, np.float32) if x.dtype.type is not dt.type: x = x.astype(dt)
I'd actually like to add a `min_dtype` keyword to asarray, We need it in several places.
That sounds like a good idea. Ralf
On Tue, Mar 3, 2015 at 7:12 PM, Ralf Gommers <ralf.gommers@gmail.com> wrote:
On Wed, Mar 4, 2015 at 1:34 AM, Charles R Harris < charlesr.harris@gmail.com> wrote:
On Tue, Mar 3, 2015 at 5:31 PM, Charles R Harris < charlesr.harris@gmail.com> wrote:
On Tue, Mar 3, 2015 at 5:21 PM, Jaime Fernández del Río < jaime.frio@gmail.com> wrote:
On Tue, Mar 3, 2015 at 4:11 PM, Charles R Harris < charlesr.harris@gmail.com> wrote:
Hi All,
This is with reference to issue #5626 <https://github.com/numpy/numpy/issues/5626>. Currently linalg.norm converts the input like so `x = asarray(x)`. This can produce integer arrays, which in turn may create problems of overflow, or the failure of the abs functions for minimum values of signed integer types. I propose to convert the input to a minimum precision of float32. However, this will be a change in behavior. I'd guess that that might not be much of a problem, as otherwise it is likely that this problem would have been reported earlier.
Thoughts?
Not sure if it makes sense here, but elsewhere (I think it was polyval) we let object arrays through unchanged.
That would still work. I'm thinking something like
x = asarray(x) dt = result_type(x, np.float32) if x.dtype.type is not dt.type: x = x.astype(dt)
I'd actually like to add a `min_dtype` keyword to asarray, We need it in several places.
That sounds like a good idea.
Not sure what idea you are referring to, but I"ve added a `precision` keyword in gh- 5634. <https://github.com/numpy/numpy/pull/5634> Chuck
participants (3)
-
Charles R Harris -
Jaime Fernández del Río -
Ralf Gommers