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# Christian Jauvincjauvin

Created Jun 15, 2011
 # Christian Jauvin # http://cjauvin.github.com # # Solutions for: # https://www.readyforzero.com/challenge # ajobwelldone+b26e9@readyforzero.com # Problem 1 s = "en3pG3+nz+A2acXKrsyDouhViP9EDQS4JQK6uJqM3rBjKEBKC3yc=AA1=LUQqRPHvQ4dopgkbb/axClP3smzVcaTkRsCqHSG5aKFQ2TbOae0t5r4nWrCVesGK1Z3yEq+dClrDwXXOiAMyW09WdCS+CaKcfu=6kv9dUFBcS4KsUIgwMiXimoBpJZSWlBzILVf4zVA=7GjRP8RXn6uKjbjAPkpFEs/mYJpeOpEnhQfPhjoscgjfL5/SQsU6+jaAf5pg9MQzZdQAEJt7Jm1541fEmnumpjmJMd/MTJ5vzBttBBA7b5rbjDX0nHdTWn8C5suYKfNyYzc6x8S6FIepoEBsMS2mKhx5BRH5jSBrYRem4iQgYARzGnCFot3jPhp3cHj7qjXBWfZZASz8YJqi2d+r393AmdGm1L9NfU2f=FJprbLwJpuE7uT7xAlQA3Ry8aHRNgNkffP29Iqb2DSoQ0PK+9LX0t37HIAhI5zvoP6b4J7yQZEQDgeNlnPQMvSjw9pLWAxQ1VUY+NMU5BLZ2Bxuma1cIsHxcx4PwHcg0u1HYPJAWM2WK=xhJP5aQSc6oNMQK4s2=6guQRVFll6rvWkXTebrdsws7m/Kpa29spUFl8XzFx0ondEMCF3byAyWj875wAI3Hn8ZY92ddTAKj0s+a4X7qSti2lA0GzePHjBjMCD5g9kZYLtB94kkbVZ6eCle/xtto4LHH8GElc5YoUi=mk3nmQ5iOL1zMWfDyRUMLq+HCXbQL9NTejNa/yTdL3sayJOlMW1T7/Jmaz1FMbfBRFzruHeMT41=Zu3nYZJ3nIP22qKrFzNkt/24RuQ+7IMVCI2
Created Oct 27, 2012
View backend.py
 import psycopg2, psycopg2.extras import little_pger as db from flask import * application = Flask('autocomplete-tribute') @application.route('/autocomplete', methods=['GET']) def autocomplete(): conn = psycopg2.connect("dbname=autocomplete-tribute user=christian", connection_factory=psycopg2.extras.RealDictConnection)
Created Nov 4, 2012
View joblib_batch_mechanism.py
 import time from math import sqrt from joblib import Parallel, delayed # Results obtained on my dual code Thinkpad laptop by using this modification: # https://github.com/cjauvin/joblib/compare/parallel_job_batch start = time.time() Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in xrange(50000)) print time.time() - start # 4.2 secs
Created Nov 9, 2012
View boltzmann.py
 # Simulator for the simple Boltzmann machine of Coursera NN Lecture 11e # Christian Jauvin - cjauvin@gmail.com from collections import defaultdict import numpy as np # weights w_v1_h1 = 2 w_h1_h2 = -1 w_h2_v2 = 1
Created Nov 23, 2012
View joblib_batch_potenza_results.py
 import time from sklearn.feature_extraction.text import CountVectorizer # brown20.txt is the Brown corpus concatenated 20x to itself (~1M lines) # Results obtained on a 24-core Linux machine start = time.time() vect = CountVectorizer() vect.fit_transform(open('brown20x.txt'), n_jobs=1) print time.time() - start # 307 seconds
Created Dec 11, 2012
View autocomplete_backend.py
 import psycopg2, psycopg2.extras import little_pger as db from flask import * application = Flask('autocomplete-tribute') @application.route('/autocomplete', methods=['GET']) def autocomplete(): conn = psycopg2.connect("dbname=autocomplete-tribute user=christian", connection_factory=psycopg2.extras.RealDictConnection)
Created Dec 11, 2012
View autocomplete_combo.js
 Ext.onReady(function() { Ext.define('Ubuntu', { extend: 'Ext.data.Model', fields: [{ name: 'release', convert: function(v, rec) { return Ext.String.format('{0} {1} - {2}', rec.raw.adjective, rec.raw.animal,
Created Dec 11, 2012
View autocomplete_where.py
 where = {} where['adjective'] = 'Lucid' # where adjective = 'Lucid' where['adjective'] = ('Warty', 'Dapper') # where adjective in ('Warty', 'Dapper') # For sequence values, the rules are: a tuple translates # to the 'in' operator (as above), a list to a PG array
Created Dec 11, 2012
View autocomplete_query.sql
 select * from ubuntu where adjective || animal || version ilike E'%lynx%' and adjective || animal || version ilike E'%04%' and adjective || animal || version ilike E'%lucid%'
Created Dec 11, 2012
View autocomplete_ubuntu.sql
 create table ubuntu ( id serial primary key, adjective text, animal text, version text ); insert into ubuntu (adjective, animal, version) values ('Warty', 'Warthog', '4.10'); insert into ubuntu (adjective, animal, version)
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