Dear all,
I was wondering is there a convenient inverse function of
np.polyval(), where I give the y value and it solves for x?
I know one way I could do this is:
import numpy as np
# Set up the question
p = np.array([1, 1, -10])
y = 100
# Solve
p_temp = p
p_temp[-1] -= y
x = np.roots(p_temp)
Just to say that, thanks to Matthew Brett, binary wheels for Mac OS X
are now available, for Python versions 2.7, 3.3, and 3.4. This means
that, if you're on that platform, you won't have to build from source!
As usual, just run `pip install pyviennacl`, and please report any
issues you encounter t
Hi Till,
Till Stensitzki writes:
> thanks for providing windows binaries, i never was able to build vienna
> cl on my own. Big question: is there interoperability between pyopencl
> and pyviennacl? I don't want to copy these big arrays around, especially
> if they are already on the device.
A
Hey,
thanks for providing windows binaries, i never was able to build vienna
cl on my own. Big question: is there interoperability between pyopencl
and pyviennacl? I don't want to copy these big arrays around, especially
if they are already on the device.
greetings
Till
__
Hi Neal,
Neal Becker writes:
> Typically, I have multiple CPU cores running in 'trivial parallel'
> mode - each running an independent point of a monte-carlo simulation.
>
> Could multiple processes multiplex use of a single GPU, using
> PyViennaCL?
As long as your OpenCL implementation allows m
As a follow up, here's an explanation of how to do this without pip or
virtualenv. To do it with virtualenv, just place the wrapper scripts in
your virtualenv's bin directory:
To build python:
Install python build dependencies (on Centos 6). Make sure to get the i686
packages:
tk-devel, tcl-devel
Typically, I have multiple CPU cores running in 'trivial parallel' mode - each
running an independent point of a monte-carlo simulation.
Could multiple processes multiplex use of a single GPU, using PyViennaCL?
___
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