wesm

"""
A little script for summarizing my Interactive Broker statements
"""

from pandas import *
from pandas.util.testing import set_trace as st

import numpy as np
from BeautifulSoup import BeautifulSoup

m1m0r1

#!/usr/bin/env python2.6
# coding: utf-8

# Twitter
import twitter

CONSUMER_KEY = ''
CONSUMER_SECRET = ''
ACCESS_TOKEN_KEY = ''
ACCESS_TOKEN_SECRET = ''

mblondel

def online_mean_variance(iterable):
    mN = 0
    mM = 0.0
    mS = 0.0

    for x in iterable:
        mN += 1
        nextM = mM + (x - mM) / mN
        mS += (x - mM) * (x - nextM)

mbostock

license: gpl-3.0
redirect: https://observablehq.com/@mbostock/mobile-patent-suits

marcelcaraciolo

from scipy import linalg
import numpy as np
from scipy.spatial.distance import cosine

#Let's define the matrix
user_ids = np.array(['Amanda', 'Anna', 'Bruno', 'Ricardo'])
item_ids = np.array(['Back to The Future', 'Conan',
                    'Lord of the Rings', 'Star Wars'])

matrix = np.matrix([

gwik

# Copyright 2011 10gen
# 
# Modified by Antonin Amand <[email protected]> to work with gevent.
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#   http://www.apache.org/licenses/LICENSE-2.0
# 

pcardune

.fb_access_token
.fbconsole.py

marcelcaraciolo

def spearman_coefficient(X, Y):
    """
    Considering the rows of X (and Y=X) as vectors, compute the
    distance matrix between each pair of vectors.

    Like  Pearson Coefficient , but compares relative ranking of preference
    values instead of preference values themselves. That is, each user's
    preferences are sorted and then assign a rank as their preference value,
    with 1 being assigned to the least preferred item.

marcelcaraciolo

import datetime
import sys
import random


def _rank_dists(ranks1, ranks2):
    """Finds the difference between the values in ranks1 and ranks2 for keys
    present in both dicts. If the arguments are not dicts, they are converted
    from (key, rank) sequences.
    """

psychemedia

# Script to generate a co-occurring tag graph from news articles via Guardian Platform API
# This is a quick hack script - just get things done... Needs refactoring/tidying...

import simplejson,urllib,csv,sys
from itertools import combinations

# D3.js json generator from https://bitbucket.org/hagberg/networkx-d3
import d3