based on rage-quit support for bash
Put the files below inside ~/.oh-my-zsh/custom/plugins/fuck
Also chmod a+x
the flip
command.
based on rage-quit support for bash
Put the files below inside ~/.oh-my-zsh/custom/plugins/fuck
Also chmod a+x
the flip
command.
=Navigating= | |
visit('/projects') | |
visit(post_comments_path(post)) | |
=Clicking links and buttons= | |
click_link('id-of-link') | |
click_link('Link Text') | |
click_button('Save') | |
click('Link Text') # Click either a link or a button | |
click('Button Value') |
Here's how to install PostgreSQL and have it run automatically at startup, on an Ubuntu 10.04 virtual machine using Vagrant. This took me a while to figure out:
Add the default lucid32 base box to your vagrant, if you haven't already:
host> vagrant box add lucid32 http://files.vagrantup.com/lucid32.box
Now make a new lucid32 virtual machine and install postgresql on it:
You could have postgre installed on localhost with password (or without user or password seted after instalation) but if we are developing we really don't need password, so configuring postgre server without password for all your rails project is usefull.
require 'luhn' | |
require 'json' | |
require 'uri' | |
require 'net/http' | |
notes = ["Note: spaces and dashes aren't allowed. If you are going to write\n", | |
"4444 4444 4444 4448 or 4444-4444-4444-4448, write:\n", | |
"4444444444444448.\n", | |
"Note 2:If your Diners Club card begins with 5 and is 16 digits,\n", | |
'it will be treated as a Mastercard.' |
function [maxtab, mintab]=peakdet(v, delta, x) | |
%PEAKDET Detect peaks in a vector | |
% [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local | |
% maxima and minima ("peaks") in the vector V. | |
% MAXTAB and MINTAB consists of two columns. Column 1 | |
% contains indices in V, and column 2 the found values. | |
% | |
% With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices | |
% in MAXTAB and MINTAB are replaced with the corresponding | |
% X-values. |
""" | |
This is a batched LSTM forward and backward pass | |
""" | |
import numpy as np | |
import code | |
class LSTM: | |
@staticmethod | |
def init(input_size, hidden_size, fancy_forget_bias_init = 3): |