オンライン広告まわりの研究へのポインタをいくつか。 以下のものから辿っていけばある程度は見つかるはず。
オンライン広告に関する論文は、国際学会では、ウェブ系・データマイニング系のWWW, WSDM, KDDや、情報検索系のSIGIR, CIKMあたりに出てくる印象。 後は、機械学習系のICMLやNIPSでもたまに出る。
# -*- coding: utf-8 -*- | |
import datetime | |
from numpy import asarray, ceil | |
import pandas | |
import rpy2.robjects as robjects | |
def stl(data, ns, np=None, nt=None, nl=None, isdeg=0, itdeg=1, ildeg=1, | |
nsjump=None, ntjump=None, nljump=None, ni=2, no=0, fulloutput=False): |
#!/usr/bin/env python | |
# (c) 2015 Productize <joost@productize.be> | |
import sys, copy, collections, codecs | |
from bs4 import BeautifulSoup | |
soup = BeautifulSoup(open(sys.argv[1])) | |
date = soup.design.date.contents[0] |
// work in progress | |
// you need a poloniex API key and secret with trading option enabled | |
// you can test it with: | |
// = polo("returnBalances","BTC") | |
// or | |
// = polo("returnBalances","all") | |
// or buy and sell: | |
// polo("BUY","BTC_LTC", 0.0251, 1) or polo("SELL","BTC_LTC", 0.0251, 1) |
#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
import csv | |
import codecs | |
import numpy as np | |
import MeCab | |
from sklearn.feature_extraction.text import TfidfVectorizer | |
from sklearn.cluster import KMeans, MiniBatchKMeans |
<html> | |
<head> | |
<link rel="stylesheet" href="/examples/stylesheets/ui-lightness/jquery-ui-1.8.16.custom.css" type="css"> | |
<script type="text/javascript" src="/examples/javascripts/jquery.min.js"></script> | |
<script type="text/javascript" src="/examples/javascripts/jquery.base64.min.js"></script> | |
<script type="text/javascript" src="/examples/javascripts/jquery-ui-1.8.16.custom.min.js"></script> | |
<script type="text/javascript" src="/examples/javascripts/jquery.tmpl.min.js"></script> | |
</head> | |
<body> |
#coding: utf8 | |
""" | |
1. Download this gist. | |
2. Get the MNIST data. | |
wget http://deeplearning.net/data/mnist/mnist.pkl.gz | |
3. Run this code. | |
python autoencoder.py 100 -e 1 -b 20 -v | |
""" | |
import numpy | |
import argparse |
using UnityEngine; | |
using UnityEngine.Networking; | |
using UnityEngine.Networking.NetworkSystem; | |
using System.Collections; | |
public class Connector : MonoBehaviour { | |
int connectionAttemptCount; | |
NetworkClient client; | |
bool errorHappened; | |
void Start () { | |
StartClient (); |
import tensorflow as tf | |
import optuna | |
import sklearn.datasets | |
from sklearn.model_selection import train_test_split | |
class TensorFlowPruningHook(tf.train.SessionRunHook): | |
def __init__(self, trial, estimator, metric, is_higher_better, run_every_steps): | |
self.trial = trial |
#include "testApp.h" | |
//-------------------------------------------------------------- | |
void testApp::setup() | |
{ | |
ofSetDataPathRoot("data/"); | |
ofBackground(0); | |
//setup recognizer | |
recognizer = zinnia::Recognizer::create(); |