| LibJitsi.start(); | |
| MediaService mediaService = LibJitsi.getMediaService(); | |
| //I assume that I have a working video and audio MediaDevice | |
| MediaDevice randomVideoDevice = createVideoDevice(); | |
| MediaDevice randomAudioDevice = createAudioDevice(); | |
| //I create the MediaFormat for each stream | |
| MediaFormat videoFormat |
| /* Code snippets from Unite 2015 - A coder's guide to spline-based procedural geometry */ | |
| /* https://www.youtube.com/watch?v=o9RK6O2kOKo */ | |
| // Optimized GetPoint | |
| Vector3 GetPoint( Vector3[] pts, float t ) { | |
| float omt = 1f-t; | |
| float omt2 = omt * omt; | |
| float t2 = t * t; | |
| return pts[0] * ( omt2 * omt ) + | |
| pts[1] * ( 3f * omt2 * t ) + |
| # begin build properties | |
| # autogenerated by buildinfo.sh | |
| ro.build.id=JSS15J | |
| ro.build.display.id=JSS15J.I9505XXUEML1 | |
| ro.build.version.incremental=I9505XXUEML1 | |
| ro.build.version.sdk=18 | |
| ro.build.version.codename=REL | |
| ro.build.version.release=4.3 | |
| ro.build.date=Tue Dec 10 14:28:08 KST 2013 | |
| ro.build.date.utc=1386653288 |
| //from: http://stackoverflow.com/questions/934012/get-image-data-in-javascript | |
| function getBase64Image(img) { | |
| // Create an empty canvas element | |
| var canvas = document.createElement("canvas"); | |
| canvas.width = img.width; | |
| canvas.height = img.height; | |
| // Copy the image contents to the canvas | |
| var ctx = canvas.getContext("2d"); |
View installed PPA Repository with a .sh script
#! /bin/sh
# listppa Script to get all the PPA installed on a system ready to share for reininstall
for APT in `find /etc/apt/ -name \*.list`; do
grep -o "^deb http://ppa.launchpad.net/[a-z0-9\-]\+/[a-z0-9\-]\+" $APT | while read ENTRY ; do
USER=`echo $ENTRY | cut -d/ -f4`
PPA=`echo $ENTRY | cut -d/ -f5`
echo sudo apt-add-repository ppa:$USER/$PPA
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
| Latency Comparison Numbers | |
| -------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
| package main | |
| import ( | |
| "fmt" | |
| "os" | |
| "os/exec" | |
| "syscall" | |
| ) | |
| func main() { |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| sudo apt-get update | |
| sudo apt-get install python-virtualenv | |
| sudo apt-get install python-dev | |
| sudo apt-get install postgresql | |
| sudo apt-get install postgresql-server-dev-9.3 | |
| sudo apt-get install redis-server | |
| sudo -u postgres createuser -s sentry | |
| sudo -u postgres psql -c "alter user sentry with password 'sentry';" |