serbrech

#!/bin/sh

# Credits goes to http://stackoverflow.com/questions/1589114/opening-a-new-terminal-tab-in-osxsnow-leopard-with-the-opening-terminal-window#answer-7911097
# I just slightly modified it to take an argument.

new_tab() {
        pwd=`pwd`
        osascript -e "tell application \"Terminal\"" \
        -e "tell application \"System Events\" to keystroke \"t\" using {command down}" \
        -e "do script \"cd $pwd; clear; $1;\" in front window" \

jaytaylor

import time
from werkzeug.serving import BaseRequestHandler

class MyFancyRequestHandler(BaseRequestHandler):
    """Extend werkzeug request handler to suit our needs."""
    def handle(self):
        self.fancyStarted = time.time()
        rv = super(MyFancyRequestHandler, self).handle()
        return rv

shmup

Experimental Generation of Interpersonal Closeness

Instructions to Subjects Included With Task Slips Packet

This is a study of interpersonal closeness, and your task, which we think will be quite enjoyable, is simply to get close to your partner. We believe that the best way for you to get close to your partner is for you to share with them and for them to share with you. Of course, when we advise you about getting close to your partner, we are giving advice regarding your behavior in this demonstration only, we are not advising you about your behavior outside of this demonstration.

In order to help you get close we've arranged for the two of you to engage in a kind of sharing game. You're sharing time will be for about one hour, after which time we ask you to fill out a questionnaire concerning your experience of getting close to your partner.

You have been given three sets of slips. Each slip has a question or a task written on it. As soon as you both finish reading these instructions, you should

0asa

from pyspark import SparkConf, SparkContext
from sklearn.datasets import make_classification
from sklearn.ensemble import ExtraTreesClassifier
import pandas as pd
import numpy as np

conf = (SparkConf()
         .setMaster("local[*]")
         .setAppName("My app")
         .set("spark.executor.memory", "1g"))

alfard

import numpy as np
import random


class Node:
	def __init__(self,t,L,R,D,S,V,M,X):
		self.t=t  
		self.L=L
		self.R=R
		self.D=D

baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

DaniSancas

Neo4j Tutorial

Fundamentals

Store any kind of data using the following graph concepts:

• Node: Graph data records
• Relationship: Connect nodes (has direction and a type)
• Property: Stores data in key-value pair in nodes and relationships
• Label: Groups nodes and relationships (optional)

martinapugliese

# Copyright (C) 2016 Martina Pugliese

from boto3 import resource
from boto3.dynamodb.conditions import Key

# The boto3 dynamoDB resource
dynamodb_resource = resource('dynamodb')


def get_table_metadata(table_name):

dirko

# Keras==1.0.6
from keras.models import Sequential
import numpy as np
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from keras.layers import Merge
from keras.backend import tf

ckoppelman

import nltk

from nltk.tokenize.treebank import TreebankWordTokenizer

class TreebankSpanTokenizer(TreebankWordTokenizer):

    def __init__(self):
        self._word_tokenizer = TreebankWordTokenizer()

    def span_tokenize(self, text):