messefor

## bias_variance_degree.ipynb

      
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                messefor
                / bias_variance_degree.ipynb
            
            
              Created
              August 16, 2016 09:48
            
          
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## check_if_installed_and_load.R
# sample of checking package installed and load all.
required.pckg <-
  c(
    'dummies',
    'tidyverse',
    'glmnet'
  )

not.installed <- !(required.pckg %in% installed.packages()[,'Package'])
if (any(not.installed)) {

## convert_datatype_in_df.R
# set function with which convert data into named vector
col.type.f <- c(vane=as.factor,
              prefecture=as.factor,
              date=as.Date,
              time=as.factor,
              ts=as.POSIXct)

# convert data type
for (col in names(col.type.f)) {
  f.conv <- col.type.f[col][[1]]

## excel_pandas.md

      
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                messefor
                / excel_pandas.md
            
            
              Last active
              December 4, 2016 11:45
            
              
                Excel Bookからデータをpandasに読み込むメモ
              
          
    pandas Excel Bookからデータ読み込み

anaconda が入っていれば、特に問題にならない


事前に必要なモジュールをインストール
$ pip install openpyxl xlrd xlwt


## 161204_matplotlib_jpfont.md

      
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                messefor
                / 161204_matplotlib_jpfont.md
            
            
              Last active
              December 4, 2016 15:21
            
              
                matplotlib 日本語設定のときに使うコマンド
              
          
    matplotlib 日本語フォント

日本語設定のときに使うコマンド
# 設定ファイルの場所確認
matplotlib.matplotlib_fname()
#> C:\Users\daisuke.oda\AppData\Local\Continuum\Anaconda3\lib\site-packages\matplotlib\mpl-data\matplotlibrc

# システムフォントファイル一覧

  
## ggplot2_multi_circle.R
library(tidyverse)
library(ggrepel)

#-------------------------
# データ作成
#-------------------------

# 円のインデックスとrを指定(r^2が中心からのユークリッド距離)
dist.vec <- c(user1=1, user2=2, user3=3, user4=4)
idx.vec <- names(dist.vec)

## broadcast_in_R.ipynb

      
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                messefor
                / broadcast_in_R.ipynb
            
            
              Created
              January 15, 2017 04:46
            
          
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## subplot_with_pandas.ipynb

      
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                messefor
                / subplot_with_pandas.ipynb
            
            
              Created
              February 11, 2017 10:20
            
              
                pandas.DataFrame.plotとsubplotを使って、複数グラフを柔軟にプロットする
              
          
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## ubcf_for_blog.R
#----------------------------------------------------------
# Rによる協調フィルタリングの実装例
#
# Brainpad技術ブログ：レコメンドつれづれ 1-2付属コード
#
#----------------------------------------------------------

library(recommenderlab)

#------------------------------------------

## hist_cum_twin.py
"""Example to show how to plot histogram with accumulate ratio."""
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# % matplotlib inline
sns.set(style="darkgrid", palette="muted", color_codes=True)

# Toy data
np.random.seed(0)
	# sample of checking package installed and load all.
	required.pckg <-
	c(
	'dummies',
	'tidyverse',
	'glmnet'
	)

	not.installed <- !(required.pckg %in% installed.packages()[,'Package'])
	if (any(not.installed)) {
	# set function with which convert data into named vector
	col.type.f <- c(vane=as.factor,
	prefecture=as.factor,
	date=as.Date,
	time=as.factor,
	ts=as.POSIXct)

	# convert data type
	for (col in names(col.type.f)) {
	f.conv <- col.type.f[col][[1]]
	library(tidyverse)
	library(ggrepel)

	#-------------------------
	# データ作成
	#-------------------------

	# 円のインデックスとrを指定(r^2が中心からのユークリッド距離)
	dist.vec <- c(user1=1, user2=2, user3=3, user4=4)
	idx.vec <- names(dist.vec)
	#----------------------------------------------------------
	# Rによる協調フィルタリングの実装例
	#
	# Brainpad技術ブログ：レコメンドつれづれ 1-2付属コード
	#
	#----------------------------------------------------------

	library(recommenderlab)

	#------------------------------------------
	"""Example to show how to plot histogram with accumulate ratio."""
	import numpy as np
	import matplotlib.pyplot as plt
	import seaborn as sns

	# % matplotlib inline
	sns.set(style="darkgrid", palette="muted", color_codes=True)

	# Toy data
	np.random.seed(0)