genn | enhanced Neuronal Network simulation environment | GPU library
kandi X-RAY | genn Summary
kandi X-RAY | genn Summary
GeNN is a GPU-enhanced Neuronal Network simulation environment based on code generation for Nvidia CUDA.
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Trending Discussions on genn
QUESTION
here's my whole code
...ANSWER
Answered 2019-Dec-11 at 12:12The code you've said you want to run, and the code you've actually run are different.
QUESTION
I cant find out the error in this activity.I am newbie to android.Please anyone can correct the error in java and xml?I have a spinner, Datepicker and list view.The spinner will show three item.Everything works fine.Te problem is whenever i search on any item and click it,It will open first item of list.Please tell me if you need anything else to resolve this issue.
My activty
...ANSWER
Answered 2018-Mar-21 at 07:38The logic you implement not going to work when you using filter. For this you have to use parent.getAdapter().getItem(position);
.
Make sure the getItem(position)
in your custom adapter returns the right data.
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QUESTION
I am trying to write a function that acts as an autocorrect. So basically somewhere in the script a user will assign a colour to a variable and then the function checks whether it is part of a list of common spelling errors for that colour and then assign the correct spelling.
...ANSWER
Answered 2017-Dec-13 at 09:47You need to add a return
value to the function, to return either the corrected colour or, if the colour does not match, some other return value. The parameter should also be called something more meaningful than variable
, such as str
(for string). Here is a revised function:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install genn
Download and unpack GeNN.zip to a convenient location, then download and install the Microsoft Visual C++ compiler and IDE from: http://www.visualstudio.com/en-us/downloads. Be sure to select the 'Desktop development with C++' configuration' and the 'Windows 8.1 SDK' and 'Windows Universal CRT' individual components. If your machine has an NVIDIA GPU, then download and install a compatible version of the Nvidia CUDA toolkit from: https://developer.nvidia.com/cuda-downloads. Note that the latest version of Visual Studio is not necessarily compatible with the latest version of the CUDA toolkit.
Ensure that the CUDA_PATH environment variable is defined and points to the location of the Nvidia CUDA toolkit installation; and that the CUDA bin directory is included in the path. These can be checked by using: ECHO %CUDA_PATH% and ECHO %PATH% respectively (although they are usully set automatically by the CUDA installer on Windows systems). If not, correct this using: SETX CUDA_PATH "[drive]\Program Files\NVIDIA GPU Computing Toolkit\CUDA[version]" and SETX PATH "%PATH%;%CUDA_PATH%.
Add the bin sub-directory of the directory in which GeNN is located to your PATH variable. For example, if you extracted GeNN to c:\Users\me\GeNN, use: SETX PATH "c:\Users\me\GeNN\bin;%PATH%".
To access a developer command prompt, use the shortcut link in: start menu -> all programs -> Microsoft Visual Studio -> Visual Studio Tools -> x64 Native Tools Command Prompt which will launch an instance of cmd.exe with a build environment already set up. Alternatively, from any cmd console window, run the vscvsrsall.bat script under the Visual C++ directory before compiling any projects.
For Arch Linux there are GeNN packages available from the AUR.
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