View kaggle_kernel_package.py
ImageHash 3.1
arrow 0.9.0
altair 1.2.0
tqdm 4.10.0
mlxtend 0.5.1.dev0
PyWavelets 0.5.0
pyLDAvis 2.0.0
TPOT 0.6.7
traitlets 4.3.1
funcy 1.7.2
View coincheckJPY.ipynb
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
View pca_tsne.ipynb
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
View recipe.apib
FORMAT: 1A
# レシピサイトWebAPIドキュメント
http://gotofritz.net/blog/weekly-challenge/restful-python-api-bottle/
## すべてのレシピのXMLを表示する [/recipes/]
### recipes_list [GET]
すべてのレシピのリストを返します。
View iris_xgboost.py
import numpy as np
import scipy as sp
import xgboost as xgb
from sklearn import datasets
from sklearn.metrics import confusion_matrix
from sklearn.grid_search import GridSearchCV
from sklearn.grid_search import RandomizedSearchCV
iris = datasets.load_iris()
trainX = iris.data[0::2,:]
View calculate_score.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
# File format : GROUPID,GOODSID,VIEWRATE,BUYRATE
# Usage : calculate_score.py target.csv predict.csv
def read_submit_file(submit_file):
viewrate = {}
View anond.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import feedparser
import pymongo
import datetime
anond_feed = feedparser.parse("http://anond.hatelabo.jp/rss")
client = pymongo.MongoClient()
db = client["anond"]
View agqr_schedule.rb
require 'open-uri'
require 'Nokogiri'
require 'pp'
uri = "http://www.agqr.jp/timetable/digital-mf.php"
html = Nokogiri::HTML(open(uri),nil,"utf-8")
html.xpath("//table[@id='timeline']/tbody/tr").each do |timeline|
puts "=============="
if timeline.xpath("th").text != ""
View agqr.rb
# -*- encoding: utf-8 -*-
#/usr/bin/env ruby
require 'yaml'
radio_dir = "/path/to/radio"
rtmpdump = "/path/to/rtmpdump"
agqr_stream_url = "rtmp://fms-base1.mitene.ad.jp/agqr/aandg22"
schedule = "schedule.yaml"
ffmpeg = "/path/to/ffmpeg"
View gmm_em.py
import math
import numpy as np
def mv_norm(x, mu, sigma):
norm1 = 1 / (math.pow(2 * math.pi, len(x)/2.0) * math.pow(np.linalg.det(sigma), 1.0/2.0))
x_mu = np.matrix(x-mu)
norm2 = np.exp(-0.5 * x_mu * sigma.I * x_mu.T)
return float(norm1 * norm2)
# test data