| require 'nokogiri' | |
| require 'open-uri' | |
| require 'sqlite3' | |
| def createcolumns(categories) | |
| categories.each do |field| | |
| db.execute ("ALTER TABLE hf ADD COLUMN #{field} {FLOAT};") |
| import grequests | |
| CRUNCHBASE_API_KEY = 'asdasdasdasdadaasdasdasdas' | |
| ''' | |
| base url to get a company's info. The individual company link loks like this: | |
| http://ec2-107-21-104-179.compute-1.amazonaws.com/v/1/company/facebook.js | |
| ''' |
| #!/usr/bin/env python | |
| #Totally copied from http://www.reddit.com/r/Python/comments/27gumv/share_your_helper_scripts/ | |
| import os, random | |
| def random_mac(): | |
| mac = [ 0x00, 0x16, 0x3e, | |
| random.randint(0x00, 0x7f), | |
| random.randint(0x00, 0xff), | |
| random.randint(0x00, 0xff) ] |
| import requests | |
| def get_file(url): | |
| response = requests.get(url) | |
| filename = url.split('/')[-1] | |
| print url | |
| print('SAVING FILE {}'.format(filename)) | |
| with open(filename, 'wb') as file: | |
| file.write(response.content) | |
| file.close() |
| # If you are running more than one instances of graylog2-server you have to select one of these | |
| # instances as master. The master will perform some periodical tasks that non-masters won't perform. | |
| is_master = true | |
| # The auto-generated node ID will be stored in this file and read after restarts. It is a good idea | |
| # to use an absolute file path here if you are starting graylog2-server from init scripts or similar. | |
| node_id_file = /etc/graylog2-server-node-id | |
| # You MUST set a secret to secure/pepper the stored user passwords here. Use at least 64 characters. | |
| # Generate one by using for example: pwgen -s 96 |
| package es.catmobil.tornofici.ui.views; | |
| import android.content.Context; | |
| import android.content.res.TypedArray; | |
| import android.os.Build; | |
| import android.text.Editable; | |
| import android.text.TextWatcher; | |
| import android.util.AttributeSet; | |
| import android.util.Log; | |
| import android.view.View; |
To demonstrate text classification with Scikit Learn, we'll build a simple spam filter. While the filters in production for services like Gmail will obviously be vastly more sophisticated, the model we'll have by the end of this chapter is effective and surprisingly accurate.
Spam filtering is the "hello world" of document classification, but something to be aware of is that we aren't limited to two classes. The classifier we will
| import os | |
| import httplib2 | |
| import argparse | |
| import tempfile | |
| import json | |
| import base64 | |
| import logging | |
| from apiclient.discovery import build | |
| from apiclient.errors import HttpError |
| """ | |
| This is a batched LSTM forward and backward pass | |
| """ | |
| import numpy as np | |
| import code | |
| class LSTM: | |
| @staticmethod | |
| def init(input_size, hidden_size, fancy_forget_bias_init = 3): |
| """ | |
| This is a batched LSTM forward and backward pass | |
| """ | |
| import numpy as np | |
| import code | |
| class LSTM: | |
| @staticmethod | |
| def init(input_size, hidden_size, fancy_forget_bias_init = 3): |