QuantumDamage

## 013.ipynb

      
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              October 22, 2018 09:55
            
              
                Nieco więcej o pozbywaniu się niepotrzebnych danych
              
          
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## 013.ipynb

      
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              October 22, 2018 09:55
            
              
                Nieco więcej o pozbywaniu się niepotrzebnych danych
              
          
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## 013.ipynb

      
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              October 14, 2018 09:05
            
              
                problem with eli5 module
              
          
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## entropia-calosc.ipynb

      
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                QuantumDamage
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              July 19, 2018 10:09
            
              
                jakbadacdane.pl - entropia-calosc
              
          
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## entropia-dane.ipynb

      
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              July 19, 2018 10:04
            
              
                jakbadacdane.pl - entropia-dane
              
          
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## not-random-forest.ipynb

      
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              July 5, 2018 13:07
            
              
                Can I generate forest with the same trees?
              
          
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## equations.tex
Silhouette Coefficient
s = \frac{b - a}{\max\{a,b\}}

Zakres wartośći
s = \begin{cases}
  1-a/b, & \mbox{jeśli } \,\,\,a < b \\
  0,  & \mbox{jeśli } \,\,\,a = b \\
  b/a-1, & \mbox{jeśli } \,\,\,a > b \\
\end{cases}

## nbp.sh
# Java
sudo add-apt-repository ppa:webupd8team/java
# Enter
sudo apt-get update
sudo apt-get install oracle-java8-installer
# Enter
# Enter
# Yes

# Spark

## dummies.py
import pandas as pd
import numpy as np

dataTrain = pd.DataFrame({'county': ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma']})

dummiesTrain = pd.get_dummies(dataTrain)

dataLive = pd.DataFrame({'county': ['Santa Cruz','Poznań', 'Pima']})

dummiesLive = pd.get_dummies(dataLive)

## aqip15-2.py
for pollutant in pollutants:
    print pollutant
    scores = []
    inputData = oneHour[[pollutant, "lat", "long"]]
    predictData = inputData[~pd.notnull(inputData[pollutant])]
    trainData = inputData[pd.notnull(inputData[pollutant])]

    X = trainData[["lat", "long"]]
    y = trainData[pollutant]
	Silhouette Coefficient
	s = \frac{b - a}{\max\{a,b\}}

	Zakres wartośći
	s = \begin{cases}
	1-a/b, & \mbox{jeśli } \,\,\,a < b \\
	0, & \mbox{jeśli } \,\,\,a = b \\
	b/a-1, & \mbox{jeśli } \,\,\,a > b \\
	\end{cases}
	# Java
	sudo add-apt-repository ppa:webupd8team/java
	# Enter
	sudo apt-get update
	sudo apt-get install oracle-java8-installer
	# Enter
	# Enter
	# Yes

	# Spark
	import pandas as pd
	import numpy as np

	dataTrain = pd.DataFrame({'county': ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma']})

	dummiesTrain = pd.get_dummies(dataTrain)

	dataLive = pd.DataFrame({'county': ['Santa Cruz','Poznań', 'Pima']})

	dummiesLive = pd.get_dummies(dataLive)
	for pollutant in pollutants:
	print pollutant
	scores = []
	inputData = oneHour[[pollutant, "lat", "long"]]
	predictData = inputData[~pd.notnull(inputData[pollutant])]
	trainData = inputData[pd.notnull(inputData[pollutant])]

	X = trainData[["lat", "long"]]
	y = trainData[pollutant]