bshillingford

// For Phillips Ambilight TV
#include <IRremote.h>

IRsend irsend;

// Raw data recorded from a few buttons:
unsigned int mute[40] =     {2700,850,500,750,500,400,500,350,1350,1200,500,400,500,350,500,350,500,400,500,350,500,350,500,400,500,350,500,350,500,400,450,400,950,350,500,800,950,2850,50,};
unsigned int power[42] =    {2700,850,500,750,500,400,500,350,1350,1200,500,400,500,350,500,350,500,400,500,350,500,350,500,400,500,350,500,350,500,400,450,400,950,350,500,800,500,350,500,2800,200,};
unsigned int netflix[38] =  {2700,850,500,750,550,300,500,350,1400,1200,550,300,550,350,500,350,500,350,550,350,500,350,500,350,500,400,900,400,500,350,500,800,950,350,550,750,500,};
unsigned int smarttv[40] =  {2700,800,500,800,500,350,500,350,1400,1200,500,350,500,350,500,400,500,350,500,350,500,400,500,350,950,800,950,350,500,350,500,400,500,350,500,800,500,2250,1200,};

bshillingford

#include <iostream>
#include <chrono>
#include <ratio>

class progbar {

private:
    int call_count = 0;

    // formatting:

bshillingford

#ifndef _LOG_H_
#define _LOG_H_

// logging utils
//
#include <iostream>
#include <ctime>

#define LOG(level) ::log_internal::_log_helper<::log_internal::loglevel:: level >(__FILE__, __LINE__, __FUNCTION__)

bshillingford

#ifndef _READNP_H_
#define  _READNP_H_
// Simple header-only library for loading serialized numpy arrays.
// Only for testing.

#include <algorithm>
#include <cassert>
#include <fstream>
#include <regex>
#include <string>

bshillingford

bshillingford

function arxiv-authors() {
    if [ $# -ne 1 ]; then
        echo "Usage: arxiv-authors arxiv_url_or_id"
        echo "Prints authors' names, one per line."
    else
        if [[ $1 == http* ]]; then
            url=$1
        else
            url=http://arxiv.org/abs/$1
        fi

bshillingford

--[[ An implementation of L-BFGS, heavily inspired by minFunc (Mark Schmidt)

This implementation of L-BFGS relies on a user-provided line
search function (state.lineSearch). If this function is not
provided, then a simple learningRate is used to produce fixed
size steps. Fixed size steps are much less costly than line
searches, and can be useful for stochastic problems.

The learning rate is used even when a line search is provided.
This is also useful for large-scale stochastic problems, where

bshillingford

#!/bin/bash

##############################
# install Torch

# export PREFIX=/home/scratch/$USER/torch
export PREFIX="/home/scratch/torchshared/torch"
mkdir -p $PREFIX

rm -rf /tmp/luajit-rocks

bshillingford

"""
Google Directions API
"""

import urllib2
import urllib
import json

class Modes:
    DRIVING="driving"

bshillingford

# -*- coding: utf-8 -*-
__author__ = 'brendan'

import collections


class property_dict(collections.Mapping):
    """
    Dictionary wrapper that adapts a dict with a wrapper allowing property-style reads. Intended
    for ease of JSON accessing.