import os
import sys
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import glob | |
import pandas as pd | |
import numpy as np | |
import category_encoders as ce | |
from sklearn.model_selection import train_test_split | |
from sklearn.ensemble import RandomForestRegressor | |
from sklearn.metrics import r2_score | |
files = glob.glob("train/*.csv") | |
data_list = [] |
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import glob | |
import pandas as pd | |
import numpy as np | |
import xgboost as xgb | |
import category_encoders as ce | |
from sklearn.model_selection import train_test_split | |
files = glob.glob("train/*.csv") | |
data_list = [] | |
for file in files: |
import os
import sys
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#成績データの入力 | |
seiseki<-matrix(c(89,90,67,46,50,57,70,80,85,90,80,90,35,40,50,40,60,50,45,55,78,85,45,55,60,55,65,80,75,85,90,85,88,92,95),7,5,byrow=TRUE) | |
colnames(seiseki)<-c("算数","理科","国語","英語","社会") | |
rownames(seiseki)<-c("田中","佐藤","鈴木","本田","川端","吉野","斎藤") | |
#ユークリッド距離を求める。distは、成績のユークリッド距離を求めている。 | |
seiseki.d<-dist(seiseki) | |
#小数点以下を丸める | |
round(seiseki.d) |
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#!/usr/bin/python3 | |
import datetime | |
import sys | |
def info_parser(lines, result): | |
row = lines[0].split(' ') | |
result['name'] = row[2] | |
result['number'] = int(row[5]) | |
result['time'] = datetime.datetime( |
Chrome 上で reveal.js で作成したスライドを pdf 化する手順メモ
- URL に ?print-pdf を追加する
- CMD + P で印刷ダイアログを表示し、出力先を pdf に設定
- 出力
出力された pdf を確認し、リンクが表示されていないなど表示がおかしい場合は以下の事を試す(ここからが本題)
reveal.js/out.html の document.write している行をコメントアウトし、css/print/pdf.css を直接追加。
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#!/usr/bin/env python3 | |
# name : fgddem.py | |
# purpose : translates digital elevation model of Fundamental Geospatial Data provided by GSI | |
# into a GDAL supported format (which has write access). | |
# license : The MIT License | |
# copyright : (c) 2012, Minoru Akagi | |
# | |
# Permission is hereby granted, free of charge, to any person obtaining a | |
# copy of this software and associated documentation files (the "Software"), | |
# to deal in the Software without restriction, including without limitation |