lbolla

'''Pipeline

(yield) -> receiver
.send -> producer
'''

import time

N = 0

jamesrcounts

String.Contains C# Edition

I enjoy playing with state machines. They are simple enough to implement quickly, and complex enough to give the implementation language a little workout. Followers of this blog will know that I've enjoyed using Finite State Machines to explore CoffeeScript.

But, when I look at the search terms leading to my blog, I see that people want to see FSMs implemented in C#, C++, or even VB. About the only language I haven't seen people looking for is CoffeeScript. I'm not too suprised, since most people searching for FSM implementations are probably students trying to cheat on thier homework. I doubt any professors are accepting CoffeeScript implemenations.

When I see these search results, I consider quickly posting a solution in the requested language. I don't mind if students crib solutions from me. I'm a big believer in learning by example. Certainly if by looking at my FSMs I can save one student from writing a switch statement, then I'll consider it a public serv

afair

==========================================          ==========================================
             TMUX COMMAND                                        WINDOW (TAB)
==========================================          ==========================================

List    tmux ls                                     List         ^b w
New          new -s <session>                       Create       ^b c
Attach       att -t <session>                       Rename       ^b , <name>
Rename       rename-session -t <old> <new>          Last         ^b l               (lower-L)
Kill         kill-session -t <session>              Close        ^b &

Deco

--[[ deepcopy.lua
    
    Deep-copy function for Lua - v0.2
    ==============================
      - Does not overflow the stack.
      - Maintains cyclic-references
      - Copies metatables
      - Maintains common upvalues between copied functions (for Lua 5.2 only)
    
    TODO

lelandbatey

Description

This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.

The script is here:

#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"

Results

lazywei

#!/bin/sh


# this script does absolutely ZERO error checking.   however, it worked
# for me on a RHEL 6.3 machine on 2012-08-08.  clearly, the version numbers
# and/or URLs should be made variables.  cheers,  zmil...@cs.wisc.edu


mkdir mosh
cd mosh

karpathy

"""
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)

baraldilorenzo

##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

farrajota

require 'nn'
 
-- you just need to provide the linear module you want to convert,
-- and the dimensions of the field of view of the linear layer

function convertLinear2Conv1x1(linmodule,in_size)
--[[
    Convert Linear modules to convolution modules. 
    
    Arguments

ndronen

#!/usr/bin/env python
"""
A quick, partial implementation of ENet (https://arxiv.org/abs/1606.02147) using PyTorch.
The original Torch ENet implementation can process a 480x360 image in ~12 ms (on a P2 AWS
instance).  TensorFlow takes ~35 ms.  The PyTorch implementation takes ~25 ms, an improvement
over TensorFlow, but worse than the original Torch.
"""

from __future__ import absolute_import