gvx

from itertools import product

def score(self, other):
	first = len([speg for speg, opeg in zip(self, other) if speg == opeg])
	return first, sum([min(self.count(j), other.count(j)) for j in 'ABCDEF']) - first

possible = [''.join(p) for p in product('ABCDEF', repeat=4)]
results = [(right, wrong) for right in range(5) for wrong in range(5 - right) if not (right == 3 and wrong == 1)]

def solve(scorefun):

timcowlishaw

package uk.ac.ucl.cs.GI15.timNancyKawal {
  class Trie[V](key: Option[Char]) {

    def this() {
      this(None);
    }

    import scala.collection.Seq
    import scala.collection.immutable.TreeMap
    import scala.collection.immutable.WrappedString

crtr0

// set-up a connection between the client and the server
var socket = io.connect();

// let's assume that the client page, once rendered, knows what room it wants to join
var room = "abc123";

socket.on('connect', function() {
   // Connected, let's sign-up for to receive messages for this room
   socket.emit('room', room);
});

ttezel

#A Collection of NLP notes

##N-grams

###Calculating unigram probabilities:

P( w_i ) = count ( w_i ) ) / count ( total number of words )

In english..

neomatrix369

Everything I Ever Learned About JVM Performance Tuning @Twitter- by Attila Szegedi

http://www.infoq.com/presentations/JVM-Performance-Tuning-twitter (video & slides)

9 Fallacies of Java Performance - by Ben Evans

http://www.infoq.com/articles/9_Fallacies_Java_Performance (video & slides)

Visualizing Java GC - by Ben Evans

http://www.infoq.com/presentations/Visualizing-Java-GC (video & slides)

Java Garbage Collection Distilled by Martin Thompson

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

monikkinom

#Source code with the blog post at http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/
import numpy as np
import random
from random import shuffle
import tensorflow as tf

# from tensorflow.models.rnn import rnn_cell
# from tensorflow.models.rnn import rnn

NUM_EXAMPLES = 10000

codekansas

from __future__ import print_function

import json
import os
import numpy as np

from gensim.models import Word2Vec
from gensim.utils import simple_preprocess
from keras.engine import Input
from keras.layers import Embedding, merge

philipperemy

from __future__ import print_function

import numpy as np
from keras.callbacks import Callback
from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from numpy.random import choice

from utils import prepare_sequences