griesmey

def squareList(xs: List[Int]): List[Int] = xs match {
  case Nil => xs
  case y :: ys => y * y :: squareList(ys)
}

def squareList1(xs: List[Int]): List[Int] =
  xs map (y => y * y)

griesmey

import math.Ordering

def msort[T](xs: List[T])(implicit ord: Ordering[T]): List[T] = {
  val n = xs.length / 2
  if (n == 0) xs
  else {
    def merge(xs: List[T], ys: List[T]): List[T] =
      (xs, ys) match {
        case (Nil, ys) => ys
        case (xs, Nil) => xs

griesmey

> emacs thing.cpp

#include <iostream>
#include <algorithm>
using namespace std;

template <class T>
class Node {
public:
  Node* right;

griesmey

from collections import Counter
import re
from sklearn.feature_extraction import DictVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
from itertools import islice, tee
from nltk.corpus import stopwords

def tokenize(sentence):
    words = re.findall("[a-zA-Z]+", sentence)

griesmey

from itertools import islice, tee
from nltk.corpus import stopwords

griesmey

from math import sqrt
import sys
from collections import defaultdict
from itertools import izip


class Point(object):
    def __init__(self, x, y, id=None):
        self.x = x
        self.y = y

griesmey

import glob
import get_data
import run_knn
from math import *


__author__ = 'Robert Griesmeyer'
__date__ = 'May 21st 2011'
__doc__ = """ This module is for the use of accuaracy checking of the\
k-nearest neighbor algorithm execution.  The parts tested are the predictions\