aparrish

bzamecnik

"""
Classifies sequences of length 10 with 20 features into 2 classes
with a single LSTM layer with 32 neurons.

See also a more involved example:
https://gist.github.com/bzamecnik/dccc1c4fdcf1c7a31757168b19c827a7
"""


from keras.layers import Input, LSTM, Dense

adamwlev

## Makes a monitor where the mean of last x oob improvements
## are used to determine early stopping. This can be ammended
## to any stopping criteria one sees as fit - consecutive x 
## negatives, more negatives than positives in last x, etc.
def make_monitor(running_mean_len):
    def monitor(i,self,args):
        if np.mean(self.oob_improvement_[max(0,i-running_mean_len+1):i+1])<0:
            return True
        else:

aessam

"""
The basic idea is that I loop through all characters and count the number of 
pixels and make <Hashtable number of pixels as a key and character as value>

The table will not have 255 value so to cover this I had to calculate the 
slope of change as a base step for the colors.
example, 	if we have 50 value and we need 255 value, then each 255/50 = 5.1
			Then with each gray color we will devied by 5 so we have index to
			be fetched from the keys of the pixels table

pudo

import re

AFRICA = [u"Africa", u"Algeria", u"Angola", u"Benin", u"Botswana",
         u"Burkina Faso", u"Burundi", u"Cameroon", u"Cape Verde",
         u"Central African Republic", u"Chad", u"Comoros", u"Congo",
         u"Djibouti", u"Egypt", u"Equatorial Guinea", u"Eritrea",
         u"Ethiopia", u"Gabon", u"Gambia", u"Ghana", u"Guinea",
         u"Guinea-Bissau", u"Ivory Coast", u"Kenya", u"Lesotho",
         u"Liberia", u"Libya", u"Madagascar", u"Malawi", u"Mali",
         u"Mauritania", u"Mauritius", u"Morocco", u"Mozambique",

pudo

import os
import gdata
import atom
import gdata.contacts
import gdata.contacts.service
import dataset

import requests
from lxml import html
from urlparse import urljoin, parse_qs

widged

chartFactory

/affini-tech/ChartFactory

Based on D3.JS and Dimple, ChartFactory provide the ability to build quickly D3.JS charts without coding any lines of javascript. Just define your dashboard in a JSON and voila !

charts: [
        {id:'chart1',
         width:800,height:250,

xAxis:{type:'Category',field: "Month",orderRule:'Date'},

mbostock

license: gpl-3.0
redirect: https://observablehq.com/@d3/icicle

hrldcpr

One-line Tree in Python

Using Python's built-in defaultdict we can easily define a tree data structure:

def tree(): return defaultdict(tree)

That's it!