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import csv
import numpy as np
from sklearn.cross_validation import KFold
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.feature_selection import SelectPercentile, f_classif
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import MultinomialNB

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import math


def createDataSet():
    dataSet = [
        [1, 1, 'yes'],
        [1, 1, 'yes'],
        [1, 0, 'no'],
        [0, 1, 'no'],
        [0, 1, 'no'],

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import math
import numpy as np


def createDataSet():
    """
    Creates a basic data set labels.

    The labels are the classification given to the points. The data
    is hardcoded in this toy example.

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(function() {
    // Source: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/isArray
    if (!Array.isArray) {
        Array.isArray = function(arg) {
            return Object.prototype.toString.call(arg) === '[object Array]';
        };
    }
    
    var _delim = '/';
    var _regexDelim = "\\/";

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#! /usr/bin/env python
import argparse
from datetime import datetime
from serial import Serial
from sqlite3 import ProgrammingError
from time import sleep

from core import DataParser, SensorReadingsDataStore, MessageFormatError
from utils import get_config, create_logger_from_config, parse_sensor_types_config

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keys = ('FacilityID', 'FacilityName', 'City', 'Zip', 'Score', 'InspDate')
facilities_data = [data[key].split('|') for key in keys]
facilities = [dict(zip(keys, row)) for row in zip(*facilities_data)]

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module.exports = function(grunt) {
  grunt.initConfig({
    pkg: grunt.file.readJSON('package.json'),

    clean: ['js', 'css', 'fonts'],

    copy: {
      main: {
        files: [
          {

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/*
  Bridge Sensor with GSM, SDCard, and RTC

  Sketch used by ARTF Sensors platform.

  Created 14 6 2014
  Modified 13 7 2014
*/

#include <LowPower.h>

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#!/bin/bash

# First unzip the disc image with Keka: Right click on the xz file and then do "Open With" and pick Keka

# Next make sure you have an empty microSD card. 4GB minimum will be required.

# List disks on system and find location of microSD card. Look for the 4GB one
diskutil list

# WARNING: Make sure you selected the correct disk. You do not want to overwrite your hard drive, 

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function r(min, max) {
    return Math.floor(Math.random() * (max - min + 1)) + min;
}

var type = 's';
var start_time = 1404313860;
var interval = 2;
var readings = 10;
for (var g = 0; g < 10; g++)
{