Mac VSCode: How to plantUML preview within markdown.
$ mkdir -p ~/java
$ cd ~/java
$ brew install maven
$ git clone https://github.com/plantuml/plantuml-server.git
$ cd plantuml-server
There doesn't seem to be a good resource online describing the issues with protocol buffers and deterministic serialization (or lack thereof). This is a collection of links on the subject.
Protocol Buffers v3.0.0. release notes:
The deterministic serialization is, however, NOT canonical across languages; it is also unstable across different builds with schema changes due to unknown fields.
Wire format ordering and map iteration ordering of map values is undefined, so you cannot rely on your map items being in a particular order.
#!/bin/bash | |
# Tom Hale, 2016. MIT Licence. | |
# Print out 256 colours, with each number printed in its corresponding colour | |
# See http://askubuntu.com/questions/821157/print-a-256-color-test-pattern-in-the-terminal/821163#821163 | |
set -eu # Fail on errors or undeclared variables | |
printable_colours=256 |
More details - http://blog.gbaman.info/?p=791
For this method, alongside your Pi Zero, MicroUSB cable and MicroSD card, only an additional computer is required, which can be running Windows (with Bonjour, iTunes or Quicktime installed), Mac OS or Linux (with Avahi Daemon installed, for example Ubuntu has it built in).
1. Flash Raspbian Jessie full or Raspbian Jessie Lite onto the SD card.
2. Once Raspbian is flashed, open up the boot partition (in Windows Explorer, Finder etc) and add to the bottom of the config.txt
file dtoverlay=dwc2
on a new line, then save the file.
3. If using a recent release of Jessie (Dec 2016 onwards), then create a new file simply called ssh
in the SD card as well. By default SSH i
Prerequisites:
Software components used:
Two awsome things were released yesterday :
Compare this to the pricing of : dropbox pro
Do your part to resist Government surveillance and take back your privacy:
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import numpy as np | |
import pylab as pl | |
import pandas as pd | |
from sklearn import svm | |
from sklearn import linear_model | |
from sklearn import tree | |
from sklearn.metrics import confusion_matrix |