hi all,
I need a good estimation of noise value for simple calculations.
I.e. when I calculate something like sin(15)+cos(80) I get a solution
with precision, for example, 1e-11.
I guess the precision depends on system arch, isn't it?
So what's the best way to estimate the value?
I guess here
I need just a single number in avarage.
I have committed some changes to NLP/NSP ralg solver from
scikits.openopt, for non-noisy funcs it works better, but for noisy
funcs vise versa, hence now my examples/nssolveVSfsolve.py doesn't work
as it should be, so I need to implement noise parameter
Hi,
I am sorry if I have missed something obvious, but is there any way in
python of doing this:
import numpy as np
a = np.arange(10)
b = np.arange(10)+1
a.data = b.data # raises error, but I hope you see what I mean
?
Thanks a lot for any pointers.
Matthew
On Feb 10, 2008 5:15 PM, Matthew Brett [EMAIL PROTECTED] wrote:
Hi,
I am sorry if I have missed something obvious, but is there any way in
python of doing this:
import numpy as np
a = np.arange(10)
b = np.arange(10)+1
a.data = b.data # raises error, but I hope you see what I mean
?
Not
On Feb 10, 2008 6:48 PM, Matthew Brett [EMAIL PROTECTED] wrote:
import numpy as np
a = np.arange(10)
b = np.arange(10)+1
a.data = b.data # raises error, but I hope you see what I mean
?
Not really, no. Can you describe your use case in more detail?
Yes - I am just writing
import numpy as np
a = np.arange(10)
b = np.arange(10)+1
a.data = b.data # raises error, but I hope you see what I mean
?
Not really, no. Can you describe your use case in more detail?
Yes - I am just writing the new median implementation. To allow
future optimization, I would
Ah, I see. You definitely do not want to reassign the .data buffer in
this case. An out= parameter does not reassign the memory location
that the array object points to. It should use the allocated memory
that was already there. It shouldn't copy anything at all;
otherwise, median(x, out=out)
Hi, I am receiving a AttributeError: incompatible shape for a
non-contiguous array error. A quick illustration of the type of code
that gives me the error is shown below:
from numpy import *
list=[i for i in range(0,27)]
c=array(list)
c.shape=(3,3,3)
On Feb 10, 2008 7:43 PM, Brad Malone [EMAIL PROTECTED] wrote:
Hi, I am receiving a AttributeError: incompatible shape for a
non-contiguous array error. A quick illustration of the type of code
that gives me the error is shown below:
from numpy
On Feb 10, 2008 7:17 PM, Matthew Brett [EMAIL PROTECTED] wrote:
Ah, I see. You definitely do not want to reassign the .data buffer in
this case. An out= parameter does not reassign the memory location
that the array object points to. It should use the allocated memory
that was already
Matthew Brett wrote:
import numpy as np
a = np.arange(10)
b = np.arange(10)+1
a.data = b.data # raises error, but I hope you see what I mean
?
Not really, no. Can you describe your use case in more detail?
Yes - I am just writing the new median implementation. To allow
future
On Feb 8, 2008 5:29 AM, Francesc Altet [EMAIL PROTECTED] wrote:
Hi,
I'm a bit confused that the sort method of a string character doesn't
allow a mergesort:
s = numpy.empty(10, S10)
s.sort(kind=merge)
TypeError: desired sort not supported for this type
However, by looking at the numpy
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