superbobry

>>> import io
>>> f = io.open("./article_categories_en.nt")
>>> f = io.TextIOWrapper(f)
>>> next(f)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib64/python2.6/io.py", line 1782, in next
    line = self.readline()
  File "/usr/lib64/python2.6/io.py", line 1858, in readline
    while self._read_chunk():

superbobry

void InitParaAndVar(int CensoringPara, int tauPara, int JPara, int MPara, double dPara[], 
		    double pPara[], double piPara[], double pdfPara[])
{
	int i, j, u;

	Censoring = CensoringPara;

	tau = tauPara;
	J = JPara;
	M = MPara;

superbobry

1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

superbobry

# -*- coding: utf-8 -*-

from collections import MutableSequence


def trace(f):
    def inner(*args, **kwargs):
        print(f.__name__)
        return f(*args, **kwargs)
    return inner

superbobry

def foo():
    try:
        foo()
    finally:
        foo()

# >>> foo()
# ???

superbobry

# See http://bioinformatics-renlab.ucsd.edu/rentrac/wiki/ChromaSig

sub init_gibbs_seed {
        # ...            
#	print("score = $score\n");

#	my ($seed, $ave_dist) = tournament_sort($id, $profiles_c,
#						$clusters_arrayref, $filtered_data);
#	my $norm = $sorted_norm[$percentile + $i * $interval]->{norm};
#	my $score = $norm / $ave_dist;

superbobry

# -*- coding: utf-8 -*-

# from __future__ import print_function

import csv
import itertools
import errno
import os
import os.path
import sys

superbobry

>>> def f((x, y)): return x + y
... 
>>> def gen():
...     yield 1
...     yield 2
... 
>>> 
>>> f(gen())
3

superbobry

open StdLabels

type 'a tree = Node of ('a * 'a tree list)

let rec make ~f init =
  let (label, forest) = f init in
  Node (label, (List.map ~f:(make ~f) forest))

and draw ~f (Node (label, forest)) =
  let rec inner = function

superbobry

# module M = Map.Make((val (module String : Map.OrderedType) : Map.OrderedType));;
module M :
  sig
    type key
    type +'a t
  end
# module M = Map.Make(String);;
module M :
  sig
    type key = String.t