fnielsen

fnielsen

import pandas as pd

df = pd.DataFrame([[1, 2, 's'], [3, 4, 't'], [4, 5, 'u']], 
                  index=[-1, 0, 1], columns=['a', 'b', 'c'])

>>> df.a    # Correct type
-1    1
 0    3
 1    4
Name: a, dtype: int64

fnielsen

fnielsen

# http://docs.cherrypy.org/stable/concepts/basics.html

import cherrypy

class HelloWorld:
    def index(self):
        return "Hello world!"
    index.exposed = True

cherrypy.quickstart(HelloWorld())

fnielsen

"""
Usage:
  Nielsen2017Linking_camera.py

Notes
-----
This script demonstrates the use of Wikidata together with
ImageNet-based deep learning classifiers. It relates to the manuscript
"Linking ImageNet WordNet Synsets with Wikidata" from 2018. 

fnielsen

# Code inspired and developed from: 
# http://streamhacker.com/2010/05/10/text-classification-sentiment-analysis-naive-bayes-classifier/

from __future__ import division
import nltk.classify, nltk.corpus, nltk.classify.util
from pylab import *

filebase = '/home/fn'

fnielsen

#########
# Tornado 
wget https://raw.github.com/facebook/tornado/master/demos/helloworld/helloworld.py
python helloworld.py

# 100 concurrent
ab -c 100 -n 1000 -k localhost.localdomain:8888/ | grep "Time taken for tests:"
# Time taken for tests:   0.709 seconds

# 5 concurrent

fnielsen

import re
import requests
import pandas as pd
import matplotlib.pyplot as plt

pd.Series({re.findall('^(.+)\n', section)[0]: len(re.findall('^\*', section, flags=re.MULTILINE)) for section in re.split('^##[^#]', requests.get('https://raw.githubusercontent.com/josephmisiti/awesome-machine-learning/master/README.md').text, flags=re.MULTILINE)[1:-1]}).plot(kind='barh', title="'Awesome' machine learning links")

plt.show()

fnielsen

import matplotlib.pyplot as plt
import networkx as nx
from pysqlite2 import dbapi2

connection = dbapi2.Connection('bredewiki-templates.sqlite3')
sql = "SELECT DISTINCT pid FROM brede WHERE (template='paper' OR template='conference_paper');"
cursor = connection.cursor()
cursor.execute(sql)
pids = [ row[0] for row in cursor.fetchall() ]

fnielsen

# wget http://neuro.imm.dtu.dk/services/bredewiki/download/bredewiki-templates.sqlite3
import matplotlib.pyplot as plt
import networkx as nx
from pysqlite2 import dbapi2

connection = dbapi2.Connection('bredewiki-templates.sqlite3')
sql = "SELECT DISTINCT tid FROM brede WHERE (template='paper' OR template='conference_paper');"
cursor = connection.cursor()
cursor.execute(sql)
tids = [ row[0] for row in cursor.fetchall() ]