Keita Onabuta konabuta

## Prepare Environment.ipynb

      
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## auto-ml-classification-Force-text-dnn.ipynb

      
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## Image-Classification-catdog-MLOps.ipynb

      
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## Factory.csv

          
            ID
            Quality
            ProcessA-Pressure
            ProcessA-Humidity
            ProcessA-Vibration
            ProcessB-Light
            ProcessB-Skill
            ProcessB-Temp
            ProcessB-Rotation
            ProcessC-Density
            ProcessC-PH
            ProcessC-skewness
            ProcessC-Time

            
              1
              0
              7
              0.27
              0.36
              20.7
              0.045
              45
              170
              1.001
              3
              0.45
              8.8

            
              2
              0
              6.3
              0.3
              0.34
              1.6
              0.049
              14
              132
              0.994
              3.3
              0.49
              9.5

            
              3
              0
              8.1
              0.28
              0.4
              6.9
              0.05
              30
              97
              0.9951
              3.26
              0.44
              10.1

            
              4
              0
              7.2
              0.23
              0.32
              8.5
              0.058
              47
              186
              0.9956
              3.19
              0.4
              9.9

            
              5
              0
              7.2
              0.23
              0.32
              8.5
              0.058
              47
              186
              0.9956
              3.19
              0.4
              9.9

            
              6
              0
              8.1
              0.28
              0.4
              6.9
              0.05
              30
              97
              0.9951
              3.26
              0.44
              10.1

            
              7
              0
              6.2
              0.32
              0.16
              7
              0.045
              30
              136
              0.9949
              3.18
              0.47
              9.6

            
              8
              0
              7
              0.27
              0.36
              20.7
              0.045
              45
              170
              1.001
              3
              0.45
              8.8

            
              9
              0
              6.3
              0.3
              0.34
              1.6
              0.049
              14
              132
              0.994
              3.3
              0.49
              9.5

## moab-tutorial.py
###

# MSFT Bonsai
# Copyright 2020 Microsoft
# This code is licensed under MIT license (see LICENSE for details)

# Moab Tutorial 1
# This introductory sample demonstrates how to teach a policy for
# controlling a ball on the plate of a "Moab" hardware device.

## AirPassengers_tidyverts.Rmd
---
title: "AirPassengers"
output: html_notebook
---


```{r}
AirPassengers %>% as_tsibble %>% autoplot
```
## 過去データの平均値

## tidyverts_peyton_manning_wiki.Rmd
---
title: "Peyton Manning Wiki Tidyverts"
output: html_notebook
---

Rの時系列モデリングのライブラリ `tidyverts` を用いた基本的なモデル構築と予測値算出値を行います。

### tidyverts とは？
[tidyverts](https://tidyverts.org/) は、大きく下記の3つのライブラリから構成されます。

## wasserstein.py
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
from scipy.stats import wasserstein_distance,beta


wasserstein_distance([1,2,3,4],[1,2,3,4,4])
x = np.linspace(0, 1, 100)
dist1 = stats.beta.pdf(x,5,5)
dist2 = stats.beta.pdf(x,8,5)
ID	Quality	ProcessA-Pressure	ProcessA-Humidity	ProcessA-Vibration	ProcessB-Light	ProcessB-Skill	ProcessB-Temp	ProcessB-Rotation	ProcessC-Density	ProcessC-PH	ProcessC-skewness	ProcessC-Time
1	0	7	0.27	0.36	20.7	0.045	45	170	1.001	3	0.45	8.8
2	0	6.3	0.3	0.34	1.6	0.049	14	132	0.994	3.3	0.49	9.5
3	0	8.1	0.28	0.4	6.9	0.05	30	97	0.9951	3.26	0.44	10.1
4	0	7.2	0.23	0.32	8.5	0.058	47	186	0.9956	3.19	0.4	9.9
5	0	7.2	0.23	0.32	8.5	0.058	47	186	0.9956	3.19	0.4	9.9
6	0	8.1	0.28	0.4	6.9	0.05	30	97	0.9951	3.26	0.44	10.1
7	0	6.2	0.32	0.16	7	0.045	30	136	0.9949	3.18	0.47	9.6
8	0	7	0.27	0.36	20.7	0.045	45	170	1.001	3	0.45	8.8
9	0	6.3	0.3	0.34	1.6	0.049	14	132	0.994	3.3	0.49	9.5
	###

	# MSFT Bonsai
	# Copyright 2020 Microsoft
	# This code is licensed under MIT license (see LICENSE for details)

	# Moab Tutorial 1
	# This introductory sample demonstrates how to teach a policy for
	# controlling a ball on the plate of a "Moab" hardware device.
	---
	title: "AirPassengers"
	output: html_notebook
	---


	```{r}
	AirPassengers %>% as_tsibble %>% autoplot
	```
	## 過去データの平均値
	---
	title: "Peyton Manning Wiki Tidyverts"
	output: html_notebook
	---

	Rの時系列モデリングのライブラリ `tidyverts` を用いた基本的なモデル構築と予測値算出値を行います。

	### tidyverts とは？
	[tidyverts](https://tidyverts.org/) は、大きく下記の3つのライブラリから構成されます。
	import matplotlib.pyplot as plt
	import numpy as np
	from scipy import stats
	from scipy.stats import wasserstein_distance,beta


	wasserstein_distance([1,2,3,4],[1,2,3,4,4])
	x = np.linspace(0, 1, 100)
	dist1 = stats.beta.pdf(x,5,5)
	dist2 = stats.beta.pdf(x,8,5)