samuell

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

samuell

defmodule ATGCCount do
  def count(sequence), do: cnt(String.to_char_list(sequence),0,0)
  def cnt([65|t],at,gc), do: cnt(t,at+1,gc)
  def cnt([84|t],at,gc), do: cnt(t,at+1,gc)
  def cnt([71|t],at,gc), do: cnt(t,at,gc+1)
  def cnt([67|t],at,gc), do: cnt(t,at,gc+1)
  def cnt([62|_],at,gc), do: {at,gc}
  def cnt([],at,gc), do: {at,gc}
  # def cnt(_,0,0), do: {0,0}
  def cnt([_|t], at, gc), do: cnt(t,at,gc)

samuell

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samuell

# -*- coding: utf-8 -*-
"""
    Animated 3D sinc function
"""

from pyqtgraph.Qt import QtCore, QtGui
import pyqtgraph.opengl as gl
import pyqtgraph as pg
import numpy as np
import sys