pkerpedjiev

.

balajikvijayan

from gensim import models

sentence = models.doc2vec.LabeledSentence(
    words=[u'so`bme', u'words', u'here'], tags=["SENT_0"])
sentence1 = models.doc2vec.LabeledSentence(
    words=[u'here', u'we', u'go'], tags=["SENT_1"])

sentences = [sentence, sentence1]

class LabeledLineSentence(object):

Andy-P

# This code is part of a presentation on streaming analytics in Julia
# It was inspired by a number of individuals and makes use of some of their ideas
#     1. FastML.com got me thinking about inline processing after 
#        reading his great Vowpal Wabbit posts
#     2. John Lanford and his fantastic Vowpal Wabbit library. 
#        Check out his NYU video course to learn more (see below)
#     3. John Myles White's presentation on online SDG and his StreamStats.jl library 
# Thank you all!

dmoliveira

import seaborn as sns
from scipy.optimize import curve_fit

# Function for linear fit
def func(x, a, b):
    return a + b * x

# Seaborn conveniently provides the data for
# Anscombe's quartet.
df = sns.load_dataset("anscombe")

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname