Yoshida Tetsuya yoshida-eth0

## example_reg_multi.py
from typing import TypeAlias

import pandas as pd
from predict_model import Estimator, LinearRegressionEstimator, MultiModel
from sklearn.datasets import load_diabetes
from sklearn.discriminant_analysis import StandardScaler

# MultiModelでの回帰モデルの実装例

# 糖尿病の進行状況のデータセット読み込み

## weighted_average.py
import math
from typing import Callable

def weighted_average(values: list[int|float], weights: list[int|float]=[], func: Callable[[int,int],int|float]|None=None) -> float:
    if func:
        weights = [func(i, len(values)) for i in range(len(values))]

    if len(values)!=len(weights):
        raise IndexError(f'values and weights have different lengths: values={len(values)} weights={len(weights)}')

## pinyin.gs
// https://github.com/zh-lx/pinyin-pro
// version 3.11.0

var n = ["zh", "ch", "sh", "z", "c", "s", "b", "p", "m", "f", "d", "t", "n", "l", "g", "k", "h", "j", "q", "x", "r", "y", "w", ""],
    h = {
  "南宫": "nán gōng",
  "第五": "dì wǔ",
  "万俟": "mò qí",
  "司马": "sī mǎ",
  "上官": "shàng guān",

## example.ipynb

      
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                カスタムLogging Handlerでメモリ使用量をCSV出力してグラフ化する
              
          
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## wakachi_zh.py
#!python

"""
requirements.txt

jieba==0.42.1
googletrans==4.0.0rc1
pandas==1.3.5
pickleDB==0.9.2
"""

## poke_wordle_solver.py
import pandas as pd


RED_GREEN = ["フシギダネ","フシギソウ","フシギバナ","ヒトカゲ","リザード","リザードン","ゼニガメ","カメール","カメックス","キャタピー","トランセル","バタフリー","ビードル","コクーン","スピアー","ポッポ","ピジョン","ピジョット","コラッタ","ラッタ","オニスズメ","オニドリル","アーボ","アーボック","ピカチュウ","ライチュウ","サンド","サンドパン","ニドリーナ","ニドクイン","ニドリーノ","ニドキング","ピッピ","ピクシー","ロコン","キュウコン","プリン","プクリン","ズバット","ゴルバット","ナゾノクサ","クサイハナ","ラフレシア","パラス","パラセクト","コンパン","モルフォン","ディグダ","ダグトリオ","ニャース","ペルシアン","コダック","ゴルダック","マンキー","オコリザル","ガーディ","ウインディ","ニョロモ","ニョロゾ","ニョロボン","ケーシィ","ユンゲラー","フーディン","ワンリキー","ゴーリキー","カイリキー","マダツボミ","ウツドン","ウツボット","メノクラゲ","ドククラゲ","イシツブテ","ゴローン","ゴローニャ","ポニータ","ギャロップ","ヤドン","ヤドラン","コイル","レアコイル","カモネギ","ドードー","ドードリオ","パウワウ","ジュゴン","ベトベター","ベトベトン","シェルダー","パルシェン","ゴース","ゴースト","ゲンガー","イワーク","スリープ","スリーパー","クラブ","キングラー","ビリリダマ","マルマイン","タマタマ","ナッシー","カラカラ","ガラガラ","サワムラー","エビワラー","ベロリンガ","ドガース","マタドガス","サイホーン","サイドン","ラッキー","モンジャラ","ガルーラ","タッツー","シードラ","トサキント","アズマオウ","ヒトデマン","スターミー","バリヤード","ストライク","ルージュラ","エレブー","ブーバー","カイロス","ケンタロス","コイキング","ギャラドス","ラプラス","メタモン","イーブイ","シャワーズ","サンダース

## beer.rb
class Ingredient
  attr_reader :original_gravity

  # @param original_gravity [Float] 初期比重
  # @param suger_content [Float] 糖度
  def initialize(original_gravity: nil, suger_content: nil)
    if original_gravity
      @original_gravity = original_gravity
    else
      @original_gravity = suger_content * SUGER.original_gravity

## seveneleven.rb
require 'uri'
require 'net/http'

class HttpCache
  def initialize(path)
    @path = path

    if File.exists?(path)
      load
    else

## ginjo2015.csv

          
            点数
            アルコール分
            日本酒度
            エキス分
            酸度
            アミノ酸度
            酢酸イソアミル
            カプロン酸エチル
            甘辛度
            濃淡度

            
              北海道
              5
              15.64
              2.7
              4.74
              1.20
              1.20
              0.94
              3.30
              -0.06
              -0.89

            
              青森県
              4
              15.53
              2.7
              4.73
              1.23
              1.23
              1.25
              2.40
              -0.08
              -0.84

            
              岩手県
              7
              15.76
              2.4
              4.86
              1.09
              1.40
              1.20
              2.84
              0.11
              -1.09

            
              宮城県
              3
              15.73
              3.0
              4.73
              1.30
              1.13
              2.70
              1.43
              -0.20
              -0.71

            
              秋田県
              6
              15.50
              3.0
              4.68
              1.10
              1.03
              2.10
              4.38
              0.04
              -1.09

            
              山形県
              8
              16.10
              3.4
              4.76
              1.30
              1.28
              1.65
              3.36
              -0.09
              -0.66

            
              福島県
              6
              16.05
              3.7
              4.70
              1.15
              1.27
              1.40
              2.80
              -0.09
              -1.03

            
              茨城県
              6
              16.00
              4.9
              4.48
              1.25
              1.08
              1.87
              2.50
              -0.31
              -0.89

            
              栃木県
              5
              16.34
              0.7
              5.32
              1.62
              1.16
              2.56
              3.38
              -0.35
              -0.01

## decision-tree.py
import pandas as pd

data_frame = pd.read_csv('test.csv')

from sklearn import tree
classifier = tree.DecisionTreeClassifier(max_depth = 2)
clf = classifier.fit(data_frame.iloc[:,1:5], data_frame.iloc[:,5:6])

f = tree.export_graphviz(clf, out_file = 'test.dot', feature_names = ["Kokugo", "Sansu", "Rika", "Shakai"], class_names = ["A", "C"], filled = True, rounded = True)
	from typing import TypeAlias

	import pandas as pd
	from predict_model import Estimator, LinearRegressionEstimator, MultiModel
	from sklearn.datasets import load_diabetes
	from sklearn.discriminant_analysis import StandardScaler

	# MultiModelでの回帰モデルの実装例

	# 糖尿病の進行状況のデータセット読み込み
	import math
	from typing import Callable

	def weighted_average(values: list[int\|float], weights: list[int\|float]=[], func: Callable[[int,int],int\|float]\|None=None) -> float:
	if func:
	weights = [func(i, len(values)) for i in range(len(values))]

	if len(values)!=len(weights):
	raise IndexError(f'values and weights have different lengths: values={len(values)} weights={len(weights)}')
	// https://github.com/zh-lx/pinyin-pro
	// version 3.11.0

	var n = ["zh", "ch", "sh", "z", "c", "s", "b", "p", "m", "f", "d", "t", "n", "l", "g", "k", "h", "j", "q", "x", "r", "y", "w", ""],
	h = {
	"南宫": "nán gōng",
	"第五": "dì wǔ",
	"万俟": "mò qí",
	"司马": "sī mǎ",
	"上官": "shàng guān",
	#!python

	"""
	requirements.txt

	jieba==0.42.1
	googletrans==4.0.0rc1
	pandas==1.3.5
	pickleDB==0.9.2
	"""
	import pandas as pd


	RED_GREEN = ["フシギダネ","フシギソウ","フシギバナ","ヒトカゲ","リザード","リザードン","ゼニガメ","カメール","カメックス","キャタピー","トランセル","バタフリー","ビードル","コクーン","スピアー","ポッポ","ピジョン","ピジョット","コラッタ","ラッタ","オニスズメ","オニドリル","アーボ","アーボック","ピカチュウ","ライチュウ","サンド","サンドパン","ニドリーナ","ニドクイン","ニドリーノ","ニドキング","ピッピ","ピクシー","ロコン","キュウコン","プリン","プクリン","ズバット","ゴルバット","ナゾノクサ","クサイハナ","ラフレシア","パラス","パラセクト","コンパン","モルフォン","ディグダ","ダグトリオ","ニャース","ペルシアン","コダック","ゴルダック","マンキー","オコリザル","ガーディ","ウインディ","ニョロモ","ニョロゾ","ニョロボン","ケーシィ","ユンゲラー","フーディン","ワンリキー","ゴーリキー","カイリキー","マダツボミ","ウツドン","ウツボット","メノクラゲ","ドククラゲ","イシツブテ","ゴローン","ゴローニャ","ポニータ","ギャロップ","ヤドン","ヤドラン","コイル","レアコイル","カモネギ","ドードー","ドードリオ","パウワウ","ジュゴン","ベトベター","ベトベトン","シェルダー","パルシェン","ゴース","ゴースト","ゲンガー","イワーク","スリープ","スリーパー","クラブ","キングラー","ビリリダマ","マルマイン","タマタマ","ナッシー","カラカラ","ガラガラ","サワムラー","エビワラー","ベロリンガ","ドガース","マタドガス","サイホーン","サイドン","ラッキー","モンジャラ","ガルーラ","タッツー","シードラ","トサキント","アズマオウ","ヒトデマン","スターミー","バリヤード","ストライク","ルージュラ","エレブー","ブーバー","カイロス","ケンタロス","コイキング","ギャラドス","ラプラス","メタモン","イーブイ","シャワーズ","サンダース
	class Ingredient
	attr_reader :original_gravity

	# @param original_gravity [Float] 初期比重
	# @param suger_content [Float] 糖度
	def initialize(original_gravity: nil, suger_content: nil)
	if original_gravity
	@original_gravity = original_gravity
	else
	@original_gravity = suger_content * SUGER.original_gravity
	require 'uri'
	require 'net/http'

	class HttpCache
	def initialize(path)
	@path = path

	if File.exists?(path)
	load
	else
	点数	アルコール分	日本酒度	エキス分	酸度	アミノ酸度	酢酸イソアミル	カプロン酸エチル	甘辛度	濃淡度
北海道	5	15.64	2.7	4.74	1.20	1.20	0.94	3.30	-0.06	-0.89
青森県	4	15.53	2.7	4.73	1.23	1.23	1.25	2.40	-0.08	-0.84
岩手県	7	15.76	2.4	4.86	1.09	1.40	1.20	2.84	0.11	-1.09
宮城県	3	15.73	3.0	4.73	1.30	1.13	2.70	1.43	-0.20	-0.71
秋田県	6	15.50	3.0	4.68	1.10	1.03	2.10	4.38	0.04	-1.09
山形県	8	16.10	3.4	4.76	1.30	1.28	1.65	3.36	-0.09	-0.66
福島県	6	16.05	3.7	4.70	1.15	1.27	1.40	2.80	-0.09	-1.03
茨城県	6	16.00	4.9	4.48	1.25	1.08	1.87	2.50	-0.31	-0.89
栃木県	5	16.34	0.7	5.32	1.62	1.16	2.56	3.38	-0.35	-0.01
	import pandas as pd

	data_frame = pd.read_csv('test.csv')

	from sklearn import tree
	classifier = tree.DecisionTreeClassifier(max_depth = 2)
	clf = classifier.fit(data_frame.iloc[:,1:5], data_frame.iloc[:,5:6])

	f = tree.export_graphviz(clf, out_file = 'test.dot', feature_names = ["Kokugo", "Sansu", "Rika", "Shakai"], class_names = ["A", "C"], filled = True, rounded = True)