start new:
tmux
start new with session name:
tmux new -s myname
# Mathieu Blondel, October 2010 | |
# License: BSD 3 clause | |
import numpy as np | |
from numpy import linalg | |
def linear_kernel(x1, x2): | |
return np.dot(x1, x2) | |
def polynomial_kernel(x, y, p=3): |
Automated analysis is the main advantage to working with a modern statically typed compiled language like C++. Code analysis tools can inform us when we have implemented an operator overload with a non-canonical form, when we should have made a method const, or when the scope of a variable can be reduced.
I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!
\
sudo apt-get install python-pip
or wget https://bootstrap.pypa.io/get-pip.py && sudo python get-pip.py
sudo pip install powerline-status
git clone https://github.com/powerline/fonts.git && cd fonts && sh ./install.sh
set rtp+=/usr/local/lib/python2.7/dist-packages/powerline/bindings/vim/
> " Always show statusline
This explains how to setup for GitHub projects which automatically generates Doxygen code documentation and publishes the documentation to the gh-pages
branch using Travis CI.
This way only the source files need to be pushed to GitHub and the gh-pages branch is automatically updated with the generated Doxygen documentation.
Get an account at Travis CI. Turn on Travis for your repository in question, using the Travis control panel.
To create a clean gh-pages
branch, with no commit history, from the master branch enter the code below in the Git Shell. This will create a gh-pages branch with one file, the README.md
in it. It doesn't really matter what file is uploaded in it since it will be overwritten when the automatically generated documentation is published to th
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
import numpy as np | |
import cPickle as pickle | |
import gym | |
# hyperparameters | |
H = 200 # number of hidden layer neurons | |
batch_size = 10 # every how many episodes to do a param update? | |
learning_rate = 1e-4 | |
gamma = 0.99 # discount factor for reward |
"""A generic module to read data.""" | |
import numpy | |
import collections | |
from tensorflow.python.framework import dtypes | |
class DataSet(object): | |
"""Dataset class object.""" | |
def __init__(self, |