Kensuke Mitsuzawa Kensuke-Mitsuzawa

## gist:3849041
#! /usr/bin/python
# -*- coding: utf-8 -*-


import sys,codecs,subprocess,readline,re
#sys.stdout = codecs.getwriter('utf-8')(sys.stdout)

## 辞書の定義
info_dic = {"structure":"none","Ga":"none","Wo":"none","Ni":"none","He":"none","To":"none","Kara":"none","Yori":"none","De":"none","Time":"none","Predict":"none"}

## translate.py
# -*- coding: utf-8 -*-
"""
You need to fill in your API key from google below. Note that querying
supported languages is not implemented.

Language Code
-------- ----
Afrikaans 	af
Albanian 	sq
Arabic 	ar

## cluster_analysis.py
#! /usr/bin/python
# -coding:utf-8 -*-

import numpy as np
from sklearn.decomposition import PCA
import pandas
import logging
import os, sys, codecs, json
from sklearn.cluster import KMeans
from sklearn import datasets

## DocumentFeatureSelection.ipynb

      
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                Kensuke-Mitsuzawa
                / DocumentFeatureSelection.ipynb
            
            
              Created
              April 9, 2016 17:43
            
              
                Feature Selection demo
              
          
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## input_test_data.csv

          
            S.No.
            Datetime
            Details

            
              1
              2010/6/7 19:01
              asd

            
              1
              2010/6/8 4:00
              dfg

            
              2
              2010/6/9 0:00
              dfg

            
              2
              2010/6/10 0:00
              gfd

            
              2
              2010/6/11 0:00
              gfd

            
              3
              2010/6/12 0:00
              gfd

            
              3
              2010/6/13 0:00
              abc

            
              4
              2010/6/14 0:00
              abc

            
              4
              2010/6/15 0:00
              def

## render.py
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import pandas

# 多次元配列をつくる
x_tensor = np.random.uniform(size=(3, 10, 2), low=1.0, high=20)
#
mark_value = [2.5, 5.5]

## render_scatter_with_iteration.py
import numpy as np
import pandas
import random
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib
import typing


def fix_min_max(x_tensor: np.ndarray,

## README.md

      
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                Kensuke-Mitsuzawa
                / README.md
            
            
              Last active
              September 1, 2021 13:30
            
              
                Running SUMO traffic simulator on Google Colab
              
          
    Using SUMO in Goolge Colab

Possible to use SUMO in Google Colab.
Procedure


bash install_sumo.sh
import your scenario data (either from google drive or upload directly)
edit run_sumo.sh


## generate_dirac_delta_distribution.py
import typing
from pathlib import Path
import pandas
from tqdm import tqdm
import os
import numpy as np


def ddf(x: np.ndarray, sig: float):
   val = []

## config.yaml
SLACK_WEBHOOK:
	#! /usr/bin/python
	# -- coding: utf-8 --


	import sys,codecs,subprocess,readline,re
	#sys.stdout = codecs.getwriter('utf-8')(sys.stdout)

	## 辞書の定義
	info_dic = {"structure":"none","Ga":"none","Wo":"none","Ni":"none","He":"none","To":"none","Kara":"none","Yori":"none","De":"none","Time":"none","Predict":"none"}
	# -- coding: utf-8 --
	"""
	You need to fill in your API key from google below. Note that querying
	supported languages is not implemented.

	Language Code
	-------- ----
	Afrikaans af
	Albanian sq
	Arabic ar
	#! /usr/bin/python
	# -coding:utf-8 -*-

	import numpy as np
	from sklearn.decomposition import PCA
	import pandas
	import logging
	import os, sys, codecs, json
	from sklearn.cluster import KMeans
	from sklearn import datasets
S.No.	Datetime	Details
1	2010/6/7 19:01	asd
1	2010/6/8 4:00	dfg
2	2010/6/9 0:00	dfg
2	2010/6/10 0:00	gfd
2	2010/6/11 0:00	gfd
3	2010/6/12 0:00	gfd
3	2010/6/13 0:00	abc
4	2010/6/14 0:00	abc
4	2010/6/15 0:00	def
	import numpy as np
	import seaborn as sns
	import matplotlib.pyplot as plt
	import pandas

	# 多次元配列をつくる
	x_tensor = np.random.uniform(size=(3, 10, 2), low=1.0, high=20)
	#
	mark_value = [2.5, 5.5]
	import numpy as np
	import pandas
	import random
	import matplotlib.pyplot as plt
	import matplotlib.patches as mpatches
	import matplotlib
	import typing


	def fix_min_max(x_tensor: np.ndarray,
	import typing
	from pathlib import Path
	import pandas
	from tqdm import tqdm
	import os
	import numpy as np


	def ddf(x: np.ndarray, sig: float):
	val = []